Projects are rarely executed exactly as planned. Often, the actual durations of a project’s activities differ from the planned ones, resulting in costs stemming from the inaccurate estimation of the activities’ completion dates. While monitoring the project at various inspection points is pricy, it can lead to better estimation of the project completion time, hence saving on costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project’s path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using Information Theory measures. We search for monitoring (inspection) points that can maximize the information about the estimated project’s duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities’ durations. An exhaustive search is performed of all possible control points to find those with the highest expected information gain on the project duration. The proposed algorithm’s complexity is not affected the number of activities, and can address large projects with hundreds or thousands of activities. Numerical experimentation and analysis of various parameters are presented.

Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is non-linear, non-stationary, and which have periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of trend-cycle. In this paper, we study a copper price prediction method using Support Vector Regression. This work explores the potential of the Support Vector Regression with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchanges. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data-sets, our results obtained indicate that the parameters (C, ε, γ) of the model Support Vector Regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, being the RMSE equal or less than the 2.2% for prediction periods of 5 and 10 days.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

The food distribution process is responsible for significant quality loss in perishable products. However, preserving quality is costly and consumes a tremendous amount of energy. To tackle the challenge of minimizing transportation costs and CO2 emissions while also maximizing product freshness, a novel multi-objective model is proposed. The model integrates a vehicle routing problem with temperature, shelf life, and energy consumption prediction models, thereby enhancing its accuracy. Non-dominated sorting genetic algorithm II is adapted to solve the proposed model for the set of Solomon test data. The conflicting nature of these objectives and the sensitivity of the model to shelf life and shipping container temperature settings are analyzed. The results show that optimizing freshness objective degrade the cost and the emission objectives, and the distribution of perishable foods are sensible to the shelf life of the perishable foods and temperature settings inside the container.

Increased smart devices in various industries is creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data. Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment. The methodology involves data cleaning, preprocessing, basics statistics, outlier, and anomaly detection. Present study presents the prediction of RUL by using various Machine Learning models like Regression, Polynomial Regression, Random Forest, Decision Tree, XG Boost. Hyper Parameter Optimization is performed to find the optimal parameters for each variable. In each of the model for RUL prediction RMSE, MAE are compared. Outcome of the RUL prediction should be useful for decision maker to drive the business decision; hence Binary classification is performed, and business case analysis is performed. Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset. Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.

Universitas Sebelas Maret (UNS) through SMART UNS Company has conducted research and development of e-motorcycle conversion using Li-ion battery pack as a substitute for ICE energy source from the conventional motorcycle. Currently, the battery-pack that used for e-motorcycle conversion is in the development phase towards commercialization. The challenge of estimating production costs is the complicated production process and storing hidden expenses that can be a problem. This hidden cost is often a missing or varied factor that costs less or more expensive. This study presents an integrated parametric cost estimation model with activity-based cost assignments to estimate production costs through cost calculations for each activity. Activity-based costs break the production process into a specific cost element for each step. Each activity's cost is put into a parametric cost estimation model to calculate the cost of each activity into the total cost of production. Cost estimation results will be analyzed using a regression method to determine which variables most affect the production cost of Li-ion battery packs for the conversion of e-motorcycles in the SMART UNS company.

In this study, fibers were extracted from different parts of the okra plant (Abelmoschus esculentus) via water- and dew-retting methods. The fibers were subjected to physical and thermal analyses. The fibers obtained from the upper part of the okra plant show higher breaking strength and lower linear density. Fibers obtained via water-retting exhibited higher breaking strength, elongation at break rates, and lower linear density values. The paper also presents the results of thermogravimetric analysis of the okra fibers. Tests were carried out in oxygen and inert gas atmospheres. The temperature range of the main thermal decomposition stage was in the 275–400°C for range thermo-oxidation and 300–425°C for pyrolysis investigation. Slight differences were found in the thermal resistances of the tested fibers, which was confirmed by an analysis using the alpha s- alpha r methodology. The calculated activation energy values show a large-spread range.

This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the heath care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization.Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.

Very well into the dawn of the fourth industrial revolution (industry 4.0), we hardly distinguish between what is artificial and what is natural (e.g. man-made virus and natural virus). Thus, the level of discombobulation among people, companies or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector, makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption in the industrial sectors remains ill-defined in both academic and non-academic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technologies convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in Education industry (Education 4.0), Energy industry (Energy 4.0), Agriculture industry (Agriculture 4.0), Healthcare industry (Healthcare 4.0), and Logistics industry (Logistics 4.0) are described. The convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries are illustrated.

The landscape of centralized cloud computing is now changing to distributed and decentralized clouds with promising impacts on energy consumption, resource availability, resilience, and customer experience. This research highlights the impacts of emerging IT trends, namely, 5G wireless technology, blockchain, and industrial Artificial Intelligence (AI) in development and realization of the next generation of cloud computing. Integration of these technologies in cyber-physical system and cloud manufacturing paradigms is explained and a unified edge-fog-cloud architecture is proposed for successful implementation in manufacturing systems.

The today competitive advantage of Ready-made garment industries depends on the ability to improve the efficiency and effectiveness of resource utilization. Ready-made garment industries have long historically adopted fewer technological and process advancement as compared to automotive, electronics and semiconductor industries. Simulation modeling of garment assembly line system has attracted a number of researchers as one way for insightful analysis of system behaviour and improving its performance. However, most of simulation studies have considered ill-defined experimental design which cannot fully explore the assembly line design alternatives and does not uncover the interaction effects of the input variables. Simulation metamodeling is an approach to assembly line design which has recently been of interest to so many researchers. However, its application in garment assembly line design has never been well explored. In this paper, simulation metamodeling of trouser assembly line with 72 operations has been demonstrated. The linear regression metamodel technique with resolution-V design was used. The effects of five factors: bundle size, job release policy, task assignment pattern, machine number and helper number on the production throughput of the trouser assembly line were studied. The increase of 28.63% of the production throughput was achieved for the best factors’ setting of the metamodel.

The fourth Industrial Revolution, well-known as “Industry 4.0”, based on the integration of information and communication technologies, has introduced significant improvements in manufacturing. However, vision systems still experience various impracticalities in dealing with the effect of complex lighting on the systems platform. Therefore, a machine vision system for automatic identification of pen parts under varying lighting conditions at a digital learning factory is proposed. The developed vision system presents a straightforward approach by effectively minimizing the environmental lighting effect on the identification process. First, the obtained information of the designed vision framework is exported to a program, where a reduction of non-uniform illumination is achieved through the implementation of Retinex image enhancement techniques. Then, the color-based Fuzzy C-means (FCM) algorithm, including improved mark watershed segmentation, is employed for pen parts object classification. Finally, the position features of the selected pen part are reported. The process applied to a total number of 210 upper pen parts (caps) and 241 lower pen parts (tubes) images under different lighting scenarios. Results indicate that average parts identification precision for cap and tube parts is different and equals to 98.64% and 95.26%, respectively. The present methodology provides a promising scheme that can be feasibly adapted for other industrial Color-based object recognition applications.

The war to technology and economic power have been the driver for industrialization in most developed countries. The first industrial revolution (industry 1.0) earned millions for textile mill owners while the second industrial revolution (industry 2.0) opened the way for tycoons and captains of industry like John D. Rockefeller, J.P. Morgan and Henry Ford. The third industrial revolution (industry 3.0) engendered technology giants like Apple and Microsoft, and made magnates of men like Steve Jobs and Bill Gates. Now, the race for the fourth industrial revolution (industry 4.0) is on and there is no option, every country whether developed or developing must participate. Many countries have positively responded to industry 4.0 by developing strategic initiatives to strengthen industry 4.0 implementation. Unlocking the country’s potential to industry 4.0 has been of interest to researchers in the recent past. However, the extent to which industry 4.0 initiatives being launched globally has never been revealed. Therefore, the present study aimed at exploring industry 4.0 initiatives through comprehensive electronic survey of literature to estimate the extend of its launching in different regions. Inferences were drawn from industry 4.0 initiatives in developed nations to be used as the recommendations for East Africa Community. Results of the survey revealed that 117 industry 4.0 initiatives have been launched in 56 countries worldwide consisting of five regions. The country’s percent of industry 4.0 initiatives as per region were: Latin America and the Caribbean (15%), North America (40%), Europe (53%), Asia and Oceania (25%), Middle East and Africa (11%). While the worldwide percent was estimated as 25%. This revealed that the big gap is existing between countries towards the race for industry 4.0.

Distributed digital manufacturing offers a solution to medical supply and technology shortages during pandemics. To prepare for the next pandemic, this study reviews the state-of-the-art for open hardware designs needed in a COVID-19-like pandemic. It evaluates the readiness of the top twenty technologies requested by the Government of India. The results show that the majority of the actual medical products have had some open source development, however, only 15% of the supporting technologies that make the open source device possible are freely available. The results show there is still considerable work needed to provide open source paths for the development of all the medical hardware needed during pandemics. Five core areas of future work are discussed that include: i) technical development of a wide-range of open source solutions for all medical supplies and devices, ii) policies that protect the productivity of laboratories, makerspaces and fabrication facilities during a pandemic, as well as iii) streamlining the regulatory process, iv) developing Good-Samaritan laws to protect makers and designers of open medical hardware, as well as to compel those with knowledge that will save lives to share it, and v) requiring all citizen-funded research to be released with free and open source licenses.

Plasma Electrolytic Oxidation (PEO) is a surface treatment, similar to anodizing, that produces thick oxide films on the surface of metals. In the present work, PEO coatings were obtained on zinc-aluminized (ZA) carbon steel using as electrolyte a solution containing sodium silicate and potassium hydroxide, and working with high current densities and short treatment times in DC mode. The surface morphology resulted the typical one of PEO layers, with the presence of a lot of pores and micro-cracks. Considering the cross section, the thickness of the coating was strongly influenced by the process parameters, with different dissolution grades of the ZA layer depending on the current density and treatment time. The PEO layer resulted mainly composed by aluminum and zinc oxides and silicates. The corrosion resistance was remarkable increased in the samples with the PEO coating.

Supplier selection/replacement strategies and optimized purchasing policies play a key role in efficient supply chain management in today’s dynamic market. Here we study supplier replacement in a one-level assembly system (OLAS) producing one type of finished product. To assemble the product, we need to provide multi-type components, but assembly will be interrupted if any single component is missing, and incoming units will get hoarded until the missing component arrives. The assembly process can be interrupted by various sources of uncertainty, including delays in component deliveries. There is consequently a non-negligible risk that the assembly process may get stopped any moment. This brings inventory-related costs, which should be minimized. Here we consider discrete lead-time distributions to mimic industry-world reality. We present a model that takes into account not only optimal assignment of component order release dates but also replacement of a critical supplier. For a given unit, we model several alternative suppliers with alternative pricing and lead-time uncertainties, and we evaluate the impact on the total assembly system. For a more general case where several suppliers may be replaced, we propose a genetic algorithm.

The production of instant green tea requires hot-water extraction, which is time consuming and contributes to losses in aromatic compounds. In this study, an ultrasonic-assisted technology was used to improve the extraction efficiency of green tea, thereby shortening extraction time from 45 to 15 min. In pure water, the dissolution of caffeine and theanine did not change significantly, but total catechin dissolution increased by 0.23 mg/mL and total tea polyphenol dissolution decreased by 3.2 mg/mL. In 76.2% ethanol, the dissolution of caffeine and theanine did not change significantly, but total catechin dissolution increased by 1.57mg/mL and total tea polyphenol dissolution decreased by 1.5 mg/mL. Additionally, we used microwave-assisted technology to further improve the extraction efficiency of green tea, which shortened the extraction time to 2 min. However, the extraction rate remained unchanged. In pure water, the dissolution of caffeine and theanine did not change significantly, but the dissolution of total catechins increased by 0.41 mg/mL and the dissolution of tea polyphenols decreased by 2.9 mg/mL. Ultrasonic treatment increased the proportion of 3-hydroxybutan-2-one, (5S)-5-(hydroxymethyl)oxolan-2-one and 2-phenylethanol, which were the main aroma compounds in tea. Microwave treatment changed the aroma compounds in tea, causing losses in aroma compounds with low boiling point and maintaining (5S)-5-(hydroxymethyl)oxolan-2-one. The taste and aroma of instant green tea improved based on sensory evaluation results.

Diffusion bonding is often used on pre-machined parts to generate internal cavities, e.g. for cooling injection molding tools close to the mold cavity. Only then, the workpieces are finished to their final dimensions. In the case of micro-process devices, however, it is essential to precisely control the deformation, as otherwise uncontrollable pressure losses will occur with channel cross-sections in the sub-millimeter range. Post-processing is not possible. The most important process parameters for diffusion bonding are temperature, dwell time and contact pressure, with the bonding temperature and contact pressure acting in opposite directions and showing a strong non-linear dependence on deformation. In addition, the deformation is influenced by a number of other factors such as the absolute size of the cross-section and the aspect ratio of the parts, the dimensions and distribution of the internal cross sections and the overall percentage of the cross-section to be bonded. In micro process engineering, small material cross-sections in the range of the materials microstructure can facilitate additional deformation mechanisms such as grain boundary sliding, which are not relevant at all for larger structures. For parts consisting of multiple layers, tolerances in thickness and roughness of multiple surfaces must be levelled, contributing to the percentaged deformation. This makes it difficult, especially in micro process engineering and in single or small series production, to determine suitable joining parameters in advance, which on the one hand do not cause unforeseen large deformations, but on the other hand reliably produce highly vacuum-tight components. Hence, a definition of a fixed percentaged deformation does not work for all kinds of components. This makes it difficult to specify parameters for surely obtain high-vacuum tight parts. For successful diffusion bonding, atoms must diffuse over the bonding planes, forming a monolithic part in which the original layers are no longer visible. Only then, mechanical properties identical to those of the base material, which has been subjected to identical heat treatment, can be achieved. In this paper, the impacts of different material cross section widths as well as of the aspect ratio on deformation were investigated. By accident, it was found that also accuracy of the temperature measurement may have a serious impact in terms of deformation.

In order to increase the material throughput of aligned discontinuous fibre composites using technologies such as HiPerDiF, stability of the fibres in an aqueous solution needs to be achieved. Subsequently, a range of surfactants, typically employed to disperse carbon-based materials, have been assessed to determine the most appropriate for use in this regard. The optimum stability of the discontinuous fibres was observed when using the anionic surfactant, sodium dodecylbenzene sulfonate, which was superior to a range of other non-ionic and anionic surfactants and single-fibre fragmentation demonstrated that the employment of sodium dodecylbenzene sulfonate did not effect on the interfacial adhesion between fibres. The use of rheometry was used to complement the study to understand the potential mechanisms of the improved stability of discontinuous fibres in aqueous suspension and it led to the understanding that the increased viscosity was a significant factor. For the shear rates employed, fibre deformation was neither expected nor observed.

In a bandsaw machine the blade guides provide additional stiffness and help to align the blade near the cutting region. Typically these are either in form of blocks made of carbide or ceramics or as sealed bearings. Abrasive particles, generated while cutting hard and brittle materials like natural stones, settle between the contact surfaces of the guides and the blade causing wear and premature failure. The hydrostatic guide system as presented in this work, is a contactless blade guiding method that uses force of several pressurized water jets to align the blade to the direction of the cut. For this investigation, cutting tests were performed on a marble block using a galvanic diamond coated bandsaw blade with the upper roller guides replaced by hydrostatic guides. The results show that the hydrostatic guides help to reduce the passive force while cutting to a constant near zero in contrast to the traditional guides. This also resulted in reduced surface roughness of the stone plates that were cut indicating a reduction in lateral vibration of the band. Additionally, it has also been shown that using hydrostatic guides the bandsaw blade can be tilted to counter the bandsaw drift opening opportunities for further research in active alignment control. This original research work has shown that the hydrostatic guide systems are capable of replacing and in fact perform better than state of the art bearing or block guides particularly for stone cutting applications.

The importance of the contribution from tourism to climate change was pointed out by the International Tourism Organization (UNWTO). By combining process-based Life Cycle Assessment (LCA) and Input-output analysis, several researches have tried to evaluate the impacts of the tourism industry as well as its products and services. Indeed, the tourism sector has a wide range of industries including travel and tour, transportation, accommodation, food and beverage, amusement, souvenirs etc. However, the existing cases did not show a breakdown of the impact on climate change. In this paper, the carbon footprint (CFP) of the Japanese tourism industry was calculated based on tourist consumption, using the Japanese Input-output table and the Japanese tourism industry. It was shown that the total emissions were approximately 136 million t-CO2 per year. The contribution ratio of each stage is as follows: Transport 56.3%, Souvenirs 23.2%, Petrol (direct emissions) 16.9%, Accommodation 9.8%, Food and Beverage 7.5%, Activities 3.0%. Then, in the breakdown, the impact is high in the following order Air transport 24.7%, Petrol (direct emissions) 16.9%,Accommodation 9.8%, Food and Beverage 7.5%, Petrol 6.1%, Textile products 5.3%, Food items 4.9%, Confectionery 4.8%, Rail transport 3.9%, Cosmetics 1.9%, Footwear 1.8%, etc. In addition to transportation, this research also highlighted especially the contribution from souvenirs, accommodation, food and beverages.

Although the concept of additive manufacturing has been proposed for several decades, momentum of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization in SLM to obtain high relative density and low surface roughness simultaneously in the previous literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. The statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. It is noted that the effects of the laser power and scanning speed on the above objective quality show highly significant, while hatch space behaves an insignificant impact. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

Replacing carbon by hydrogen is a huge step towards reducing CO₂ emissions in the iron- and steel-making industry. The reduction of iron oxides using hydrogen plasma smelting reduction as an alternative to conventional steel-making routes has been studied at Montanuniversitaet Leoben, Austria. The aim of this work was to study the slag formation during the reduction process and the reduction behaviour of iron oxides. Furthermore, the reduction behaviour of iron ore during continuous feeding was assessed. Mixtures of iron ore and calcined lime with a basicity of 0, 0.8, 1.6, 2.3, and 2.9 were melted and reduced by hydrogen. The off-gas composition was measured during the operations to calculate the process parameters. The reduction parameters, namely the degree of reduction, degree of hydrogen utilisation, produced iron, and slag, are presented. The results of the batch-charged experiments showed that at the beginning of the reduction process, the degree of hydrogen utilisation was high, and then, it decreased over the operation time. In contrast, during the continuous-feeding experiment, the degree of hydrogen utilisation could be kept approximately constant. The highest degrees of reduction and hydrogen utilisation were obtained upon the application of a slag with a basicity of 2.3. The experiment showed that upon the continuous feeding of iron ore, the best conditions for the reduction process using hydrogen could be applied.

Bandsaws either use fibre or ceramic block or sealed bearings as blade guides. This works well for cutting metals, wood and plastics. However, highly abrasive particles generated while cutting stones, settle between the contacts of the blade and the guides causing wear and premature failure. Hydrostatic guide system as presented in this work, is a contactless blade guiding method that uses force of several pressurized water jets to keep the blade cutting in a straight line. For this investigation, cutting tests were performed on a marble block using a galvanic diamond coated bandsaw blade with the upper roller guides replaced by hydrostatic guides. The results show that the hydrostatic guides help to reduce the passive force to a constant near zero in contrast to the bearing guides. This also resulted in reduced surface roughness of the stone plates that were cut. Additionally, it has also been shown that using hydrostatic guides the bandsaw blade can be tilted to counter the bandsaw drift. This original research work has shown that the hydrostatic guide systems are capable of replacing and in fact perform better than the state of the art bearing or block guides specially for stone cutting applications.

The present study evaluates the manufacturing parameters effects on the tensile properties of material composed by polylactic acid (PLA) with wood fibers known as Timberfill. The specimens were built through fused filament fabrication (FFF). The influence of four printing parameters (Layer height, Fill density, Printing velocity, and Orientation) are considered through a L27 Taguchi orthogonal array in order to reduce experimental runs. Tensile test is applied to obtain the response variable used as output results to perform the ANOVA calculations. Fill density is the most influential parameter on the tensile strength, followed by building orientation and layer height, whereas the printing velocity shows no significant influence. The optimal set of parameters and levels is found, being 75% fill density, 0○Z-axis orientation, 0.4 mm layer height, and 40 mm/s velocity as the best combination. Applying this combination showed 9.37 MPa in maximum tension. Lastly, five solid Timberfill specimens manufactured via injection molding technology were also tested and the results compared to the printed samples. The values of the elastic modulus, elastic limit, and maximum tension of the injected samples were almost twofold of those were obtained for the FFF samples, but the maximum elongation of injected specimens was fell sharply.

Technological innovation has led to the development of increasingly efficient and complex industrial plants. To manage this complexity, it is necessary to define an integrated vision of the socio-technological system that includes: technological, human and organizational component. Petrochemicals can be considered one of the most complex socio-technical systems that deserve special attention to high risk management, especially during the emergency conditions. Traditional safety management models only consider static systems, while new resilience engineering models evaluate the performance variability developed between different actions. One of the recent development methods is the Functional Resonance Analysis Method (FRAM) that identifies the pairs between the functions. FRAM unfortunately is a qualitative model, this research integrates this model with the Performance Shaping Factors (PSFs) and with the Bayesian approach to identify the performance variability of the system. The analysis aims to develop a system that improves safety analysis. The proposed model is applied in a case study of an emergency in a petrochemical company.

With an increasing demand for quality of care and lower costs, hospitals are looking for industry-based methods to improve efficiency in their processes. This study aims to reduce waste in a public hospital in Mexico by improving the medical supply process for the operating room. To this end, a lean healthcare (LH) implementation following the DMAIC approach (Define-Measure-Analyze-Improve-Control) is carried out. We analyze the value stream of the supply process, including main surgical procedures and their related medical supplies, and identify different causes of inefficiency, which are evaluated and controlled through different tools, including a value stream map, Kanban, and the 5S program. As a result, five types of waste were reduced. Over-processing requests were reduced by 15.3%, defective identification numbers were reduced by 46.5%, redundant processing was improved by 94.8%, near 2.8% of the unnecessary inventory was reduced, and transportation waste was reduced by up to 16.7%. Finally, the lead-time for the main supplies was reduced by 33 days. This work demonstrates that LH and DMAIC are effective in reducing waste and are highly conducive to improving sustainability in healthcare processes. Moreover, it provides practical insights for practitioners regarding the implementation of LH in public hospitals in developing countries.

This article aims to serve as a guide for the construction of supply chain simulation models designed with a lean approach, using Promodel software. To achieve this, a supply chain was designed for a fictitious company located in the City of Celaya, Guanajuato and a set of suppliers located in different cities within the same State. It was used as a google tool to define the distances between each of the companies. As a final result, a representative model of a supply chain was obtained, as well as a methodology that allows the construction of lean supply chains regardless of the number of companies that comprise it. The effect of the variability in the delivery times between suppliers was incorporated into the simulation model, as well as an equation that calculates the pollution emissions of the vehicles that integrate the network that moves the products between the companies. With this work it is possible to represent networks of supply chains of real world companies, where the variability and contamination factor is included, to facilitate the decision making regarding the number of vehicles, inventory levels, quantities to be shipped, frequency in the shipments, etc. with the purpose of contaminating as little as possible and at the same time preventing interruptions in the supply chain using the least amount of resources possible.

Hospitals face challenges to improve efficiency in order to meet an increasing demand for high quality of care and low costs. Industry-based methods such as lean healthcare (LH) are implemented to improve healthcare systems. This study presents a LH implementation following the DMAIC approach (Define-Measure-Analysis-Improve-Control) in a Mexican public hospital, and contributes to the literature by analyzing the relation of waste reduction and sustainability. We focused on improving the medical supply chain from a temporary warehouse (TW) to the operating room (OR). Therefore, we analyzed the value stream including main surgical procedures and their related medical supplies, and identified different causes of inefficiency, which were evaluated and controlled. As a result, five types of waste were reduced through different tools including: value stream map, Kanban, 5’s, among others. Over-processing requests were reduced 15.3%; similarly, defective identification numbers were reduced up to 46.5%, redundant processing was improved by 94.8%, unnecessary inventories were reduced near to 2.8% of the TW inventory, and transportation waste was reduced up to 16.7%. As a consequence, the lead-time for the main supplies was reduced 33 days. Results indicate that LH and DMAIC are effective to reduce waste and highly conducive to improve healthcare process sustainability.

Standard Ethernet (IEEE 802.3 and the TCP/IP protocol suite) is gradually applied in industrial control system (ICS) with the development of information technology. It breaks the natural isolation of ICS, but contains no security mechanism. A modified intrusion detection system (IDS), which is strongly correlated to specific industrial scenario, is necessary for modern ICS. On the one hand, this paper outlines attack models, including infiltration attacks and our creative forging attack. On the other hand, we proposes a hierarchical IDS, which contains a traffic prediction model and an anomaly detection model. The traffic prediction model, which is based on autoregressive integrated moving average (ARIMA), can forecast the traffic of ICS network in the short term and precisely detect the infiltration attacks according to abnormal changes in traffic pattern. The anomaly detection model using one-class support vector machine (OCSVM) is able to detect malicious control instructions by analyzing the key field in EtherNet/IP packets. The experimental results show that the hierarchical IDS has an outstanding performance in detecting infiltration attacks and forging attack compared with other two innovative IDSs.

The history of Artificial Intelligence (AI) development dates to the 40s. The researchers showed strong expectations until the 70s, when they began to encounter serious difficulties and investments were greatly, reduced. With the introduction of the Industry 4.0, one of the techniques adopted for AI implementation is Machine Learning (ML) that focuses on the machines ability to receive data series and learn on their own. Given the considerable importance of the subject, researchers have completed many studies on ML to ensure that machines are able to replace or relieve human tasks. This research aims to analyze, systematically, the literature on several aspects, including publication year, authors, scientific sector, country, institution, keywords. Analyzing existing literature on AI is a necessary stage to recommend policy on the matter. The analysis has been done using Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software have been used to complete them. Literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by USA and the increasing interest after the birth of Industry 4.0.

In Safety Management System (SMS), the risk management plays a key role for the prevention of accidents. The aim of this paper is to propose a Safety Management model using a system dynamic approach to update conventional industrial safety into the new industrial safety 4.0 that is time to developed. This study analyzes some safety 4.0 aspects lacked in the Bhopal incidental event by considering different data detected in the industrial Plant. The model proposed in this paper discusses the relationships among the main causes that have contributed to the occurrence of the incidental event studied, such as broken safety devices, inadequate personnel experience, operator decisions, manager production strategy, policy decision, as deduced from the relevant literature about Bhopal incidental dynamic.

The current rapidly changing and highly competitive market has put companies under a great pressure not only to be successful, but also to sustain their success into the future. In addition, in recent years, companies have become more aware of the fact that it is no longer enough to take care of economic aspects, being crucial to also take care of environmental and social aspects in order to actually succeed and lead in the current and future markets. In this context, companies are urged to move towards more innovative manufacturing practices that maintain a healthy balance among economic, environmental and social performances, which are the three pillars of the sustainability performance. To give some insight into this issue, a Systematic Literature Review (SLR) is conducted in this paper regarding the current trends in the field, doing special focus on the link between lean-green manufacturing and the different sustainability aspects. The SLR concluded that lean and green implementations as stand-alone systems are usually not enough to ensure the required balance between the three pillars of sustainability, suggesting further combining them into a single approach. Researchers expect to achieve further improvements in the sustainability performance moving towards the next level of sustainability.

Additive manufacturing (AM) is a promising new technology that is having a very fast growth from home workshops to high-tech cutting-edge factories. As any manufacturing technique, adequate metrology services are needed to assure the quality of items manufactured by AM. One of the most widely used instruments to measure the characteristics of surfaces manufactured with AM is the confocal microscope. In this paper, authors present a whole calibration procedure for confocal microscopes designed to be implemented preferably in workshops or industrial environments rather than in research and development departments. Because of that, it is as simple as possible. The procedure is designed without forgetting any of the key aspects that need to be taken into account and based on classical reference material standards. These standards can be easily found in industrial dimensional laboratories and easily calibrated in accredited calibration laboratories.

The 21^st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 was explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done from multidisciplinary databases: Google scholar, ScienceDirect, Scopus, Sage, Taylor & Francis and Emerald insight. Results of the electronic survey showed that 35 disruptive technologies were quantified and 13 key technologies: Internet of things, Big data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The study identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. The study proposes the need to address integration of industry 4.0 concepts into the current education system.

The 21^st century has witnessed a number of incredible changes ranging from the way of life and the technologies that emerged. Currently, we have entered a new paradigm shift called industry 4.0 where science fictions have become science facts, and technology fusion is the main driver. Therefore, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this paper, disruptive technologies of industry 4.0 have been explored and quantified in terms of the number of their appearances in literature. This research mainly aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enlighten the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management from both academia and business was done from publication databases: Google scholar, ScienceDirect, Scopus, Sage, Taylor & Francis and Emerald insight. The results of the study show that 35 disruptive technologies were quantified and 13 key technologies: Internet of things, Big data, 3D printing, Cloud computing, Autonomous robots, Virtual and augmented reality, Cyber physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones and Biotechnology were identified. Moreover, both technical and personal skills to be imparted into the human workforce for industry 4.0 were identified. The study reveals the need to investigate the capabilities and the readiness of some developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. In addition, the study proposes the need to address the ways for integration of industry 4.0 concepts into the current education system.

Precipitation processes are technologies commonly used in hydrometallurgical plants to recover metals or to treat wastewaters. Moreover, solid-liquid separation technologies, such as thickening or filtering, are relevant unit operations, included in the precipitation technologies. These methods are strongly dependent on the characteristics of the solid precipitates formed during the specific precipitation reaction. One of these characteristics is the particle size distribution (PSD) of the solid precipitates which are fed into a solid-liquid separation process. Therefore, PSD determination is a typical practice for the characterization of the slurries generated in a precipitation plant. Furthermore, the precipitates generated in these processes have a colloidal or aggregation behavior, depending on the operational conditions. Nevertheless, the conventional methods used to estimate PSD (e.g., laser diffraction and/or ciclosizer) have not been designed to measure particles that tend to aggregate or disaggregate, since they include external forces (e.g., centrifugal, agitation, pumping and sonication). These forces affect the true size of the aggregates formed in a unit operation, thereby losing representativity in terms of aggregates particle size. This study presents an alternative method of measuring the size distribution of particles with aggregation behavior, particularly, by using non-invasive microscopy and image processing and analysis. The samples used have been obtained from an experimental precipitation process by applying sulfidization to treat the cyanide-copper complexes contained in a cyanidation solution. This method has been validated with statistical tools and compared with a conventional analysis based on laser diffraction. Our results show significant differences between the methods analyzed, demonstrating that image processing and analysis by microscopy is an excellent and non-invasive alternative to obtaining size distribution of aggregates in precipitation processes.

Light alloys machining is a widely implemented process that have usually used in presence of cutting fluids to reduce the wear impact and increase tool life. However, current environmental protection policies require their elimination in order to improve process sustainability. This fact forces to work under aggressive cutting conditions, producing adhesion wear that affects the integrity of the part surface. This study describes cutting tool wear mechanisms in machining of UNS A92024 samples under dry cutting conditions. EDS analysis showed the different composition of the adhered layers, while roughness was also positively affected by the change of the cutting geometry produced in the tool.

Drying food involves complex physical atmospheric mechanisms with non-linear relations from the air-food interactions and those relations are strongly dependent on the moisture contents and the type of food. Such dependence makes it complex to design suitable dryers dedicated to a single drying process. To streamline the design of a novel compact food-drying machine, a heat pump dryer component design optimization algorithm was developed as a subprogram of a Computer Aided Engineering tool. The algorithm requires inputting food and air properties, the volume of the drying container and the technical specifications of the heat-pump off-the shelf components. The heat required to dehumidify the food supplied by the heat exchange process from condenser to evaporator, and the compressor’s requirements (refrigerant mass flow rate and operating pressures) are then calculated. Compressors can then be selected based in the volume and type of food to be dried. The algorithm is shown via a flow chart to guide the user through 3 different stages: Changes in drying air properties, Heat flow within dryer and Product moisture content. Example results of how different compressors are selected for different type of produces and quantities (Agaricus Blazei mushroom with 3 different moisture contents or fish from Thunnini tribe) conclude this article.

Abstract: In order to accelerate deployment of distributed recycling by providing low-cost feed stocks of granulated post-consumer waste plastic, this study analyzes an open source waste plastic granulator system. It is designed, built and tested for its ability to convert post-consumer waste, 3-D printed products and waste into polymer feedstock for recyclebots of fused particle/granule printers. The technical specifications of the device are quantified in terms of power consumption (380 to 404W for PET and PLA, respectively) and particle size distribution. The open source device can be fabricated for less than USD$2000 in materials. The experimentally-measured power use is only a minor contribution to the overall embodied energy of distributed recycling of waste plastic. The resultant plastic particle size distributions were found to be appropriate for use in both recyclebots and direct material extrusion 3-D printers. Simple retrofits are shown to reduce sound levels during operation by 4dB-5dB for the vacuum. These results indicate that the open source waste plastic granulator is an appropriate technology for community, library, makespace, fab lab or small business-based distributed recycling.

With the focus of great concern of the sustainable development, its evaluation system has become an important operational strategy and practical values. For the purpose of obtaining the stronger indicators and the larger contribution ones, evaluation indicators screening is carried out using interval estimation model, which takes location of production and service facilities of company A as an example. And the weight value of each indicator is further explored, which can provide an direction of decision-making. The result shows that this screening method provides a more scientific evaluation method for enterprise location, decision-making basis for sustainable development of enterprises, and a solid foundation for the construction of the post-evaluation system. The present work implies that this screening method is affected, to different degrees, by the ability, knowledge reserve of the evaluators, which should be more systematic and standardized, and the concept of sustainable development should be strengthened.

Composites with HDPE and PLA matrix have been tested to analyse the effect of natural fillers (wood flour, recycled waste paper and a mix of both fillers) and temperature on polymer degradation. Composting tests have been performed in both mesophilic (35°C) and thermophilic (58°C) conditions. Degradation development has been evaluated through mass variation, TGA and DSC. HDPE, as expected, did not display any relevant variation, confirming its stability under our composting conditions. PLA is sensibly influenced by temperature and humidity, with higher reduction of Mw when composting is performed at 58°C. Natural fillers seem to influence degradation process of composites, already at 35°C. In fact, degradation of fillers at 35°C allows a mass reduction during composting of composites, while neat PLA do not display any variation.

Magnesium is one of the lightest structural metal used in different industrial sector and many works are present in literature about the study of its reinforcement by fillers addition. Basalt fibres are natural fillers with good mechanical properties, excellent resistance to high temperature and lower cost than carbon fibres. For these aspects, in the last years they are increasingly used in the production of composite materials with polymeric matrices. However, very few information are presents in literature about the use of basalt fibres as reinforcement in metal matrix composite materials. It is well known that the impregnation of fibres reinforcement affects the mechanical behavior of composites materials. The aim of this study is to investigate the impregnation and the behavior of basalt fibres in a magnesium alloy composite material manufactured by a centrifugal casting technique.

Several factors influence traffic congestion and overall traffic dynamics. Simulation modeling has been utilized to understand the traffic performance parameters during traffic congestions. This paper focuses on driver behavior of route selection by differentiating three distinguishable decisions, which are shortest distance routing, shortest time routing and less crowded road routing. This research generated 864 different scenarios to capture various traffic dynamics under collective driving behavior of route selection. Factors such as vehicle arrival rate, behaviors at system boundary and traffic light phasing were considered. The simulation results revealed that shortest time routing scenario offered the best solution considering all forms of interactions among the factors. Overall, this routing behavior reduces traffic wait time and total time (by 69.5% and 65.72%) compared to shortest distance routing.

The calculation of the release of liquid hazardous chemicals storage tanks is an important part of the quantitative risk assessment of accidents. This paper mainly establishes a continuous real–time release model based on the instantaneous mass flow Q_m model. Meanwhile, the software function module was analyzed, and programming software was developed using C# language for model solving. A series of experiments for repeated leakage tests was designed and the discharges through three small holes with different heights for 200 s were observed. The results show that the continuous real–time leakage model is effective, and the deviation between theoretical and experimental release amounts are within a reasonable range. The higher the liquid level above the leak hole is, and the smaller the height of the leak hole from the ground is, the greater the flow rate at the leak orifice is and the smaller discharge rate change is. Therefore, the deviation between the theoretical release amount M_t and the experimental average release amount Ma is greater while the height of the leak hole from the ground is smaller, which indicates that the smaller the distance from the leak orifice to the ground, the greater the influence of the empirical discharge coefficient C₀ on the release amount M.

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

Industrial manipulators and parallel robots are often used for tasks like drilling or milling, that require three translational, but only two rotational degrees of freedom (“3T2R”). While kinematic models for specific mechanisms for these tasks exist, a general kinematic model for parallel robots is still missing. This paper presents the definition of the rotational component of kinematic constraints equations for parallel robots based on two reciprocal sets of Euler angles for the end-effector orientation and the orientation residual. The method allows to completely remove the redundant coordinate in 3T2R tasks and to solve the inverse kinematics for general serial and parallel robots with the gradient-descent algorithm. The functional redundancy of robots with full mobility is exploited using nullspace projection.

This paper aims at providing a multisource data analysis, including direct data collection, focussed on daily average distances covered with motorised mobility. Its results can be used as a basis for policies involving a shift towards new propulsions, electric motors or hybrid electric vehicles (HEV) for road vehicles. A number of variables influence the propensity of drivers to acquire or use electric traction, even the option of plug-in hybrid electric vehicles (PHEV). This paper addresses one of such variable: the compliancy of electric traction regarding both hybrid plug-in solutions and full-electric vehicles, in addition to the autonomy of batteries (range), with the daily travels by road vehicles, mainly by automobiles. We want to understand whether the constraints leading towards a greater independence from crude oil rather than constraints concerning emissions, mainly in urban contexts, might be compliant with the habitual daily trips of drivers. We also want to understand if these daily trips have varied much during recent years and the consequences they may have on operational costs of plug-in automobiles. We are well aware that the average distances do not represent the actual daily runs of vehicles; yet similar distributions of daily distances for different case studies indicate that a high percentage of trips respond to certain features. After introducing a general overview of road-motorised mobility in Italy, the paper compares data from other studies to provide an indication of average daily driving distances. This reveals how different recent analyses converge on a limited range of average road distances covered daily by Italians, which is compliant with ranges allowed by electric batteries, provided that their low energy density in comparison with that of oil-derived fuels do not imply a significant increase in vehicle mass. Subsequently, average distances in some EU Countries are taken from the literature, and the results are also compared with U.S. data. The study extends the analysis of trends on the use of automobiles and road-vehicles to the international context by also addressing average daily distances covered for freight transport in some EU Countries, thereby providing a further basis for comparison and for understanding whether the daily motorised mobility can be considered as a stable phenomenon. Finally, an analysis is provided of the economic operational advantages from using plug-in vehicles. The main aim of this paper is thereafter to investigate the average daily motorised mobility of single vehicles – so not an aggregated motorised mobility as collected by some statistics – by using private motorised vehicles in Italy, with related trends; thereafter, to compare these data with those obtained from other countries, making use of both existing research studies and directly collected data; the final aim is to understand both the compliance of daily activities based on the use of automobiles with the autonomy of batteries (range) and to calculate some economic outcomes.

Force reduction is one of the most important benefit of applying ultrasonic vibration on milling. However, most of studies so far are limited to experimental investigation. In the current study, an analytical predictive model on cutting forces in ultrasonic vibration-assisted milling is proposed. The three types of tool-workpiece criteria are considered based on the instantaneous position and velocity of tool center. Type I criterion indicates that there is no contact if the instantaneous velocity is opposite to tool rotation direction. Type II criterion checks whether the vibration displacement is larger than the instantaneous uncut chip thickness. Type III criterion considers the overlaps between current and previous tool paths due to vibration. If none of these criteria is satisfied, milling forces are nonzero. Then the calculation is performed by transforming milling and tool geometry configuration to orthogonal cutting at each instant. The orthogonal cutting forces are predicted through the exhaustive search of shear angle and calculation of shear flow stress on tool-chip interface. The axial force is then calculated based on tool geometry, and the milling forces in feed, cutting, and axial directions are calculated after coordinate transformation. The proposed predictive force model in ultrasonic vibration-assisted milling is validated through comparison to experimental measurements on Aluminum alloy 2A12. The predicted values are able to match the measured milling forces with high accuracy of average difference of 13.6% in feed direction and 13.8% in cutting direction.

This study contributes to the literature by exploring challenges to implementing ISO 20000-1 in an emerging economy context, and suggests ways to overcome these challenges. A survey-based methodology was adopted. The data were analyzed using principal component analysis. The results indicated that senior management support was the most significant challenge for the successful implementation of IT Service Management (ITSM) systems. Other significant challenges were the justification of significant investment, premium customer support, co-operation and co-ordination among IT support teams, proper documentation, and effective process design The findings help managers introduce IT service management system (ISO 20000-1:2011) as well as improving IT service delivery system in IT support organizations for managing big data in an emerging economy. In the future, cross-firm and cross-country studies on challenges to ISO 20000 can be conducted. Also, interpretive structural model (ISM) can be formulated to examine the interrelationships among the identified challenges to ISO 20000.

In laser-assisted milling, higher temperature in shear zone softens the material potentially resulting in a shift of mean residual stress, which significantly affects the damage tolerance and fatigue performance of product. In order to guide the selection of laser and cutting parameters based on the preferred mean residual stress, inverse analysis is conducted by predicting residual stress based on guessed process parameters, which is defined as the forward problem, and applying iterative gradient search to find process parameters for next iteration, which is defined as the inverse problem. An analytical inverse analysis is therefore proposed for the mean residual stress in laser-assisted milling. The forward problem is solved by analytical prediction of mean residual stress after laser-assisted milling. The residual stress profile is predicted through the calculation of thermal stress, by treating laser beam as heat source, and plastic stress by first assuming pure elastic stress in loading process, then obtaining true stress with kinematic hardening followed by the stress relaxation. The variance-based recursive method is applied to solve inverse problem by updating process parameters to match the measured mean residual stress. Three cutting parameters including depth of cut, feed per tooth, and cutting speed, and two laser parameters including laser-tool distance and laser power, are updated with respected to the minimization of resulting residual stress and measurement in each iteration. Experimental measurements are referred on the laser-assisted milling of Ti-6Al-4V grade 5 and ELI. The percentage difference between experiments and predictions is less than 5% for both materials, and the selection is completed within 50 loops.

Considering the manufacturing of automotive components, there exists a dilemma around the substitution of traditional Cast Iron (CI) with lighter metals. Nowadays, aluminium alloys, being lighter compared to traditional materials, are considered as a more environmentally friendly solution. However, the energy required for the extraction of the primary materials and manufacturing of components is usually not taken into account in this debate. In this study, an extensive literature review has been performed to estimate the overall energy required for the manufacturing of an engine cylinder block using (a) cast iron and (b) aluminium alloys. Moreover, data from over 100 automotive companies, ranging from mining companies to consultancy firms, have been collected in order to support the soundness of this investigation. The environmental impact of the manufacturing of engine blocks made of these materials is presented with respect to the energy burden; the “cradle-to-grave approach” has been implemented to take into account the energy input of each stage of the component lifecycle starting from the resource extraction and reaching to the end-of-life processing stage. Our results indicate that although aluminium components contribute towards reduced fuel consumption during their use phase, the vehicle distance needed to be covered in order to compensate for the up-front energy consumption related to the primary material production and manufacturing phases is very high. Thus, the substitution of traditional materials with lightweight ones in the automotive industry should be very thoughtfully evaluated.

Nitride and carbide are the second phases which play an important role in the performance of bearing steel, and their precipitation behavior is complicated. In this study, TiN-MC_x precipitations in GCr15 bearing steels were obtained by non-aqueous electrolysis, and their precipitation mechanisms were studied. TiN is the effective heterogeneous nucleation site for Fe₇C₃ and Fe₃C, therefore, MC_x can precipitate on the surface of TiN easily, its chemistry component consists of M₃C and M₇C₃ (M = Fe, Cr, Mn) and Cr₃C₂. TiN-MC_x with high TiN volume fraction, TiN forms in early stage of solidification, and MC_x precipitates on TiN surface after TiN engulfed by the solidification advancing front. TiN-MC_x with low TiN volume fraction, TiN and MC_x form in late stage of solidification, TiN can not grow sufficiently and is covered by a large number of precipitated MC_x particles.

Cloud-based CNC is an emerging paradigm of Industry 4.0 where computer numerical control (CNC) functionalities are moved to the cloud and provided to manufacturing machines as a service. Among many benefits, C-CNC allows manufacturing machines to leverage advanced control algorithms running on cloud computers to boost their performance at low cost, without need for major hardware upgrades. However, a fundamental challenge of C-CNC is how to guarantee safety and reliability of machine control given variable Internet quality of service, especially on public Internet networks. We propose a three-tier redundant architecture to address this challenge. We then prototype tier one of the architecture on a 3D printer successfully controlled via C-CNC over public Internet connections, and discuss follow-on research opportunities.

Nitride and carbide are the second phases which play an important role in the performance of bearing steel, and their precipitation behavior is complicated. In this study, TiN-MC_x precipitations in GCr15 bearing steels were obtained by non-aqueous electrolysis, and their precipitation mechanisms were studied. TiN is the effective heterogeneous nucleation site for Fe₇C₃ and Fe₃C, therefore, MC_x can precipitate on the surface of TiN easily, its chemistry component consists of M₃C and M₇C₃ (M = Fe, Cr, Mn) and Cr₃C₂. TiN-MC_x with high TiN volume fraction, TiN forms in early stage of solidification, and MC_x precipitates on TiN surface after TiN engulfed by the solidification advancing front. TiN-MC_x with low TiN volume fraction, TiN and MC_x form in late stage of solidification, TiN can not grow sufficiently and is covered by a large number of precipitated MC_x particles.

In the fashion field, the use of electroplated small metal parts such as studs, clips and buckles is widespread. The plate is often made of precious metal, such as gold or platinum. Due to the high cost of these materials, it is strategically relevant and of primary importance for manufacturers to avoid any waste by depositing only the strictly necessary amount of material. To this aim, Companies need to be aware of the overall number of items to be electroplated so that it is possible to properly set the parameters driving the galvanic process. Accordingly, the present paper describes a Machine Vision-based method able to automatically count small metal parts arranged on a galvanic frame. The devised method relies on the definition of a proper acquisition system and on the development of image processing-based routines. Such a system is then implemented on a counting machine is meant to be adopted in the galvanic industrial practice to properly define a suitable set or working parameters (such as current, voltage and deposition time) for the electroplating machine and, thereby, to assure the desired plate thickness from one side and to avoid material waste on the other.

Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. In the presented work, an industrial exoskeleton for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is proposed, exploiting compliant actuation at the shoulder joint to increase safety and transparency in human-robot cooperation. A hierarchic model-based controller is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectory tracking, while an outer fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization.

Future cyber-physical systems may extend over broad geographical areas, like cities or regions, thus requiring the deployment of large real-time networks. A strategy to guarantee predictable communication over such networks is to synthesize an offline time-triggered communication schedule. However, this synthesis problem is computationally hard (NP-complete), and existing approaches do not scale satisfactorily to the required network sizes. This article presents a segmented offline synthesis method which substantially reduces this limitation, being able to generate time-triggered schedules for large hybrid (wired and wireless) networks. We also present a series of algorithms and optimizations that increase the performance and compactness of the obtained schedules while solving some of the problems inherent to segmented approaches. We evaluate our approach on a set of realistic large-size multi-hop networks, significantly larger than those considered in the existing literature. The results show that our segmentation reduces the synthesis time up to two orders of magnitude.

The development of hydrogen plasma smelting reduction as a CO₂ emission-free steel-making process is a promising approach. This study presents a concept of the reduction of hematite using hydrogen thermal plasma. A laboratory scale and pilot scale hydrogen plasma smelting reduction (HPSR) process are introduced. To assess the reduction behavior of hematite, a series of experiments has been conducted and the main parameters of the reduction behavior, namely the degree of hydrogen utilization, degree of reduction and the reduction rate are discussed. The thermodynamic aspect of the hematite reduction is considered and the pertinent calculations have been carried out using FactSage^TM 7.2. The degree of hydrogen utilization and the degree of reduction were calculated using the off-gas chemical composition. The contribution of carbon, introduced from the graphite electrode, ignition pin and steel crucible, to the reduction reactions was studied. The degree of reduction of hematite, regarding H₂O, CO and CO₂ as the gaseous reduction products, is determined. It is shown that the degree of hydrogen utilization and the reduction rate were high at the beginning of the experiments, then decreased during the reduction process owing to the diminishing of iron oxide. Conducting experiments with the high basicity of slag B₂=2 led to a decrease of the phosphorus concentration in the produced iron.

Engineering the supply chain requires a design that possesses the flexibility of a complex adaptive system, consisting of interlinking architecture, with external dimensions and system germane internal elements. The complexity of the subject, the multiple environments, dimensions, elements and concepts, require a research that does not set any limits to the conceptual, analytical or empirical nature of the existing approaches present in practice. This present the rational for applying a taxonomy approach to investigate the integration engineering of supply chain architecture, design and engineering, and building a framework for integrating the existing supply chain approaches. The objectives of this paper are to critically analyse the key supply chain concepts and approaches, to assess the fit between the research literature and the practical issues of supply chain architecture, design and engineering, and to develop a methodology that could be used by practitioners when integrating supply chain architecture and design with strategy engineering. Taxonomy approach is applied to consider criteria for strategy architecture, hierarchical strategy design, strategy engineering, and integration of supply chain architecture, design and engineering as a conceptual system. The results from this paper derived with the findings that the relationship between supply chain architecture, design and engineering is weak and challenges remain in the process of adapting and aligning operations. This paper also derived with a novel approach for addressing these obstacles, based on a new methodology. The novelty that derives from this paper is a methodology for integrating supply chain architecture, design and engineering, with criteria that enable decomposing and building a digital (new and non-existent) supply chain as a system. The paper revealed a number of tools and mechanism which enabled the development of a new methodology for integrating the architecture, design and engineering of a supply chain. The review derived with improvements to current and existing theories for analysing interdependencies within and between their individual contexts. This issue is addressed with a hierarchical method for network design, applied for building and combining the integration criteria.

This paper outlines a new methodology for developing strategy for supply chain integration of Autonomous Electrical Vehicles (AEV) to the Internet of Things (IoT). The methodology consists of external architecture and internal design that anticipates the business strategy in the development process. The methodology is designed to anticipate the impact of developments in new road transport technologies, such as Tesla Truck or Tesla Pickup. Since the methodology is designed to anticipate the impact of non-existing technologies, it represents green-field analysis. Green-field is defined as a new and non-existent operation. Green-field strategy architecture in this paper is presented as a process of accepting the world and acting upon that version of the world. The results of the analysis are presented as pathways and outcomes, emerging from the interrelated relationship between AEV and IoT. The emerging methodology is applied through two case studies to evaluate the impact to environment, performance and operationalisation. The methodology proposes architecture and design for integrating AEV and IoT in the supply chain strategy, and a set of new evaluation criteria that promote acceptance of Artificial Intelligence (AI) in the design process. The main contribution to knowledge is a new methodology for integrating AEV and the IoT to the supply chains. The paper applies interplay between inductive and deductive case study and grounded theory approach to build upon the concept of supply chain architecture and contribute to knowledge to the topic of formulating green-field integrated AEV- IoT supply chain strategy.

The Internet-of-Things (IoT) enables enterprises to obtain profits from data but triggers data protection questions and new types of cyber risk. Cyber risk regulations for the IoT however do not exist. The IoT risk is not included in the cyber security assessment standards, hence, often not visible to cyber security experts. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. The outcome of such self-assessment need to define a current and target state, prior to creating a transformation roadmap outlining tasks to achieve the stated target state. In this article, a comparative empirical analysis is performed of multiple cyber risk assessment approaches, to define a high-level potential target state for company integrating IoT devices and/or services. Defining a high-level potential target state represent is followed by a high-level transformation roadmap, describing how company can achieve their target state, based on their current state. The transformation roadmap is used to adapt IoT risk impact assessment with a Goal-Oriented Approach and the Internet of Things Micro Mart model. The main contributions from this paper represent a transformation roadmap for standardisation of IoT risk impact assessment; and transformation design imperatives describing how IoT companies can achieve their target state based on their current state with a Goal-Oriented approach. Verified by epistemological analysis defining a unified cyber risk assessment approach. These can be used for calculating the economic impact of cyber risk; for international cyber risk assessment approach; for quantifying cyber risk; and for planning for impact of cyber-attacks, e.g. cyber insurance. The new methods presented in this paper for applying the roadmap include: IoT Risk Analysis through Functional Dependency; Network-based Linear Dependency Modelling; IoT risk impact assessment with a Goal-Oriented Approach; and a correlation between the Goal-Oriented Approach and the IoTMM model.

This paper developed a new theory for supply chain architecture, and engineering design that enables integration of the business and supply chain strategies. The architecture starts with individual supply chain participants and derives insights into the complex and abstract concept of green-field integration design. The paper presented a conceptual system for depicting the interactions between business and supply chain strategy engineering. The system examines the decisions made when engineering the business strategy, with regards to the supply chain design. The system derived with a new understanding of how strategies are integrated, and what are the implications for engineering successful strategies. The study revealed that supply chain design is not considered in great detail before architecting the business strategies. Thus, companies consequentially experience supply chain problems that are likely to be detrimental to the growth potentials. The paper also derived with the findings that proactive and pre-emptive involvement of supply chain participants in the strategy engineering process, would lead to a more robust strategic design.

The focus of this paper is on supply chain strategy formulation. A conceptual theory approach is used for investigating and identifying the relationship between multiple elements, dimensions, forces and factors that influence and affect the supply chain strategy formulation in Greenfield context, specific to the slate mining industry. The research study involved secondary data review and series of 20 qualitative interviews, followed by 2 group discussions, one with mining and transportation experts external to the supply chain and one group discussion with supply chain internal experts. Through critical analysis, a number of problems emerge and the process of addressing these problems, results in a new framework for evaluating the relationship between business and supply chain strategy, specific to Greenfield project and integration design context.

A Micro holes in a diamond are presented by using a homemade femtosecond (fs) Yb:KGW laser. An fs laser source was used emitting pulse duration of 230 fs at 1030 nm wavelength, whereas the spot size amounted to 8.9 μm. Parameters like pulse energy, and pulse number were varied over a wide range in order to evaluate their influence both on the micro hole geometry like hole diameter, circularity, taper angle, and on the drilling quality. Hourglass-shaped micro holes whose diameters decrease and increase again after a certain depth have important applications. The results demonstrate the feasibility of extending the drilling of an hourglass-shaped hole in a diamond sample, which has similar diameters at the hole entrance (92 μm) and exit (95 μm), but a much smaller diameter (28 μm) at a certain waist section inside the hole.

Objective:The application of information technology (IT( is fundamental in the hospitals to stay competitive.In this regard, recognizing the main risks to the implementation of IT in hospitals can provide vastopportunities to improve its efficiency and help to make strategic decisions. This study aimed tosearch for the main risks of implementation of IT projects in the hospitals of Tehran. Methods: This was a practical and cross-sectional study which was conducted in the 18 hospitals of Tehran,Iran, 2018; in which a sample of 65 members were studied. The required data were collected using a questionnaire to examine seven main risks, including market, project management, human resources, technical, organizational, financial, strategic risks. The collected data were analyzed using SPSS 19.0. Additionally, the method used to test the risks in this study was structural equation modeling, which was ran using LISREL 9.30. Results: The results showed that among the seven main risks of to the implementation of IT in hospitals, the highest and lowest means were related to the human resource risks and the market risks, respectively. Also, according to the SEM, human resource risks and market risks had the highest and lowest effects, respectively. Conclusion: Announcing the use of IT in the hospitals, holding conferences about new IT developments with employees, suitable training, encouraging them to use IT tools, providing a motivating atmosphere to use IT tools for employees, are a few effective ways of overcoming the human resource risks.

The NCO functional group of 3-isocyanatoproply triethoxysilane (IPTS) and the OH functional group of DOPO-BQ were used to conduct an addition reaction. Following completion of the reaction, triglycidyl isocyanurate (TGIC) was introduced to conduct a ring-opening reaction. Subsequently, a sol-gel method was used to take place a hydrolysis- condensation reaction on TGIC-IPTS-DOPO-BQ to form a hyperbranched nitrogen–phosphorous–silicon (HBNPSi) flame retardant. This flame retardant was incorporated into a polyurethane (PU) matrix to prepare a hybrid material. Fourier-transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), limiting oxygen index (LOI), UV-VIS spectrophotometry, and Raman analysis were conducted to structure characterization and analyzed transparency, thermal stability, flame retardancy, and residual char to understand the flame retardant mechanism of prepared hybrid materials. After the flame retardant was added, the maximum degradation rate decreased from −36 wt%/min to −17 wt%/min, the integral procedure decomposition temperature (IPDT) increased from 348 ℃ to 488 ℃, and the char yield increased from 0.7 to 8.1 wt%. The aforementioned results verified that thermal stability of PU can be improved after adding HBNPSi. The LOI analysis indicated that the pristine PU was flammable because the LOI of pristine PU was only 19. When the content of added HBNPSi was 40%, the LOI value was 26; thus the PU hybrid became nonflammable.

Digital technologies have changed the way supply chain operations are structured. In this article, we develop design principles to show determining factors for an Internet-of-Things approach within Supply Chain Management. From the design principles, the article derives a new model for the Industrial Internet of Things supply chains. The focus is on Small and Medium Enterprises (SMEs). This research design results in a new process of compounding knowledge from existing supply chain models and adapting the cumulative findings to the concept of supply chains in the Industrial Internet of Things. The paper outlines the design principles for developing cognition in the process of integrating SME’s digital supply chains in the Industrial Internet of Things (IIoT) and the Industry 4.0 (I4.0).

In this paper we describe many important aspects connected with lubricants, namely: the most cheapest organic lubricant in the word, how effectively save lubricant between pairs of friction, what kind of rational shapes for the lubricant must be, what it will be connected with wear and tear using the new organic lubricant (for this purpose formulas and curves are shown in computer program MathCad with calculation). Moreover, there is one physical model which helps to catch car exhausted gases. In conclusion, some recommendations will be given to realize them into the practice during the operation for the different mechanisms including ecology.

The dye effluent is usually difficult to be degraded by conventional wastewater treatment in leather industry. In order to develop efficient and cost-effective treatment methods, we evaluate the effect of white-rot fungus immobilization for dye decolorisation in this paper. The Phanerochaete chrysosporium BKM-F-1767 was used for immobilization. This research found that the white-rot fungus immobilization had an obviously decolorisation effect in dye wastewater treatment, and plant carriers such as sorghum stalk and corn cob were helpful to the growth of Phanerochaete chrysosporium in white-rot fungus immobilization. Due to the stability and recyclability, the white-rot fungus co-immobilization was considered as the most suitable treatment for decolorisation of dye effluent which enjoyed the advantages of both adsorption immobilization and entrapment immobilization. Furthermore, the dye decolorisation evaluation was carried out to find the most suitable carrier for co-immobilization, and it found that sorghum stalk - calcium-alginate gel spherical particle (SS-CGPB) has better decolorisation effect than corn cobs - calcium-alginate gel spherical particle (CC-CGPB), and the dye decolorisation rate was 86.77%. After 5 cycles, the dye decolorisation rate was 85.87% which indicated the SS-CGPB preserved functional integrity successfully. By further analyzing the biodegradation process with white-rot fungus immobilization, the intermediate products were observed and the degradation pathway of acid golden yellow dye molecular was proposed. The results showed that the C-N single bonds attached to the central benzene in the dye molecule were attacked and destroyed in white-rot fungus co-immobilization treatment, thus the structure of dye molecule could be successfully degraded into small molecules which would be more easily treated by conventional treatment methods. Therefore, the white rot fungus co-immobilization might be appropriate for pre-treatment as an important biotechnology for the advanced treatment of dye effluent.

The energy efficiency of grinding depends on the appropriate selection of cutting conditions, grinding wheel and workpiece material. Additionally, the estimation of specific energy consumption is a good indicator to control the energy consumed during the grinding process. Consequently, this study develops a model of material removal rate to estimate the specific energy consumption based on the measurement of active power consumed in a plane surface grinding of C45K with different thermal treatments and AISI 304. This model identifies and evaluates the power dissipated by sliding, ploughing and chip formation in a industrial-scale grinding process. Furthermore, the instantaneous positions of the abrasive grains during cutting are described to study the material removal rate. The estimation of specific chip formation energy is similar to that described by other authors in laboratory scale, which allows to validate the model and experiments. Finally, the results show that the energy consumed by sliding is the main phenomenon of energy dissipation in industrial-scale grinding process, where it is denoted that sliding energy by volume unity decreases as the depth of cut and speed of workpiece increase.

Two major sample configurations are adopted in all the instruments for fabric hand measurement, flat sample as in KES and FAST types machines, and wrinkled sample as in PhabrOmeter. This paper compares the two sample types to examine which one if any offers a better coverage and reflection of the fabric sensory attributes. Fabrics have unique behaviors of drape, wrinkle and tactile sense which are entirely due to the simultaneous occurrence of both in-plane membrane deformation and out-of-plane bending deformation in multiple curvature. Such singular deformation mode cannot be detected by any machines using flat sample, whereas during a PhabrOmeter test, the fabric sample genuinely produces drape, wrinkle in addition to other related deformations. This paper then introduced the theoretical research pertaining to the measurement. Then a split sample experiment is conducted to demonstrate the importance of the internal connections in fabric during drape and wrinkle processes. As such fabric interconnection will be barely disclosed during tests using flat samples, another important advantage of PhabrOmeter is hence clearly shown.

Electron Beam Melting (EBM) is an increasingly used Additive Manufacturing (AM) technique employed by many industrial sectors, including the medical device and aerospace industries. In-process EBM monitoring for quality assurance purposes has been a popular research area. Electronic imaging has recently been investigated as one of the in-process EBM data collection methods, alongside thermal/ optical imaging techniques. Despite certain capabilities of an electronic imaging system have been investigated, experiments are yet to be carried out to benchmark one of the most important features of any imaging systems – spatial resolution. This article addresses this knowledge gap by: (1) proposing an indicator for the estimation of spatial resolution which includes the Backscattered Electrons (BSE) information depth, (2) estimating the achievable spatial resolution when electronic imaging is carried out inside an Arcam A1 EBM machine, and (3) presenting an experimental method to conduct edge resolution evaluation with the EBM machine. Analyses of experimental results indicated that the spatial resolution was of the order of 0.3mm-0.4mm when electronic imaging was carried out at room temperature. It is believed that by disseminating an analysis and experimental method to estimate and quantify spatial resolution, this study has contributed to the on-going quality assessment research in the field of in-process monitoring of the EBM process.

Electron Beam Melting (EBM) is an increasingly used Additive Manufacturing (AM) technique employed by many industrial sectors, including the medical device and aerospace industries. In EBM process monitoring, data analysis for processed layer quality evaluation is currently focused on the extraction of information from the raw data collected in-EBM process, i.e. thermal/ optical / electronic images, and the comparison between the collected data and the Computed Tomography (CT)/ microscopy images generated post-EBM process. This article postulates that a stack of bitmaps could be generated from the 3D model at a range of Z heights during file preparation of the EBM process, and serve as a reference image set. In-EBM process comparison between that and the workpiece images collected during the EBM process could then be used for quality assessment purposes. In addition, despite the extensive literature on 3D model slicing and contour generation for AM process preparation, no methods regarding image generation from cross sections of the 3D models have been disseminated in details. This article aims to address this by presenting a piece of 3D model-image generation software. The software is capable of generating binary 3D model reference images with user-defined Region-of-Interest (ROI) of the processing area, and Z heights of the model. It is envisaged that this 3D model-reference image generation ability opens up new opportunities in quality assessment for the in-process monitoring of the EBM process.

Electron Beam Melting (EBM) is an increasingly used Additive Manufacturing (AM) technique employed by many industrial sectors, including the medical device and aerospace industries. The application of this technology is, however, challenged by the lack of process monitoring and control system that underpins process repeatability and part quality reproducibility. An electronic imaging system prototype has been developed to serve as an EBM monitoring technique, the capabilities of which have been verified at room temperature and at 320+10°C. Nevertheless, in order to fully assess the applicability of this technique, the image quality needs to be investigated at a range of elevated temperatures to fully understand the influence of thermal noise due to heat. In this paper, electronic imaging pilot trials at elevated temperatures, ranging from room temperature to , were carried out. Image quality measure Q of the digital electron images was evaluated, and the influence of temperature was investigated. In this study, raw electronic images generated at higher temperatures had greater Q values, i.e. better global image quality. It has been demonstrated that, for temperatures between , the influence of temperature on electronic image quality was not adversely affecting the visual clarity of image features. It is envisaged that the prototype has significant potential to contribute to in-process EBM monitoring in many manufacturing sectors.

There is increasing use of wood-based composites in industry not only because of the shortage of solid wood, but above all for their better properties such as: strength, aesthetic appearance, etc. compared to wood. Medium density fibreboard (MDF) is a wood-based composite that is widely used in the furniture industry. The goal of the research conducted was to determine the effect of the type of coating on the drill cutting blades on the value of thrust force (F_t), cutting torque (M_c), cutting tool temperature (T) and surface roughness of the hole in drilling MDF panels. In the tests three types of carbide drills (HW) were used: not coated, TiAlN coated and ZrN coated. The measurement of both the thrust force and the cutting torque was carried out using an industrial piezoelectric sensor. The temperature of the cutting tool in the drilling process was measured using an industrial temperature measurement system using a K-type thermocouple. It was found that the value of the maximum temperature of the tool in the drilling process depends not only on the cutting speed and feed rate, but also on the type of coating of the cutting tool. The value of both the cutting torque and the thrust force is significantly influenced by the value of the feed rate and the type of drill coating. The effect of varying plate density on the surface roughness of the hole and the variation of the value of the thrust force is also discussed. The results of the investigations were statistically analysed using a multi-factorial analysis of variance (ANOVA).

To improve the effect of multi-point stretch forming of sheet metal, it is proposed in this paper to replace fixed ball head with swinging ball head. According to the multi-point dies with different arrangements, this research establishes the finite element models of the following stretch forming, i.e. fixed ball heads with conventional arrangement, swinging ball heads with conventional arrangement, swinging ball heads with declining staggered arrangement, and swinging ball heads with parallel staggered arrangement and then numerical simulation is performed. The simulation results show that by replacing fixed ball head with swinging ball head, the surface indentation of the formed part was effectively suppressed, the stress and tension strain distribution of the formed part was improved and the forming quality was improved; that the thickness of the elastic pad was reduced, the springback was reduced and the forming accuracy was improved; and that when the ball head was applied to multi-point die with staggered arrangement, better forming result was achieved, where the best forming result was achieved in combining the swinging ball heads with the multi-point die with parallel staggered arrangement. The forming experiments were carried out, and the experimental results were consistent with the trend of numerical simulation results, which verified the correctness of the numerical simulation.

The increased consumption of food requiring thermoformed packaging means that the packaging industry demands customized solutions in terms of shapes and sizes to make the packaging unique. In particular, the food industry increasingly requires more transparent packaging, with greater clarity and a better presentation of the product features they contain. However, in turn, the differentiation of products is sought through the geometry and final finish of the product, as well as the arrangement of food inside the packaging. In addition, these types of packaging usually include ribs in the walls to improve physical properties, however they also affect the final aesthetics of the product. In accordance with this, this research study analyses by studying the mechanical properties of different relief geometries that can affect not only the aesthetics but also their strength. For this purpose, tensile and compression tests have been carried out. The results provide comparative data on the reliefs studied and show that there are different shapes, sizes and layout.

In the context of food packaging design, customization enhances the value of a product by meeting the needs of the consumer. Personalization is also linked to adaptation. This makes it possible to improve the properties of the packaging from various points of view: functional, aesthetic, economic and ecological. Currently the functional and formal properties of packaging are not investigated among themselves, however the study of both properties are the basis for creating a new concept of personalized and sustainable product. In accordance with this approach, the conceptual design procedure of packaging with personalized and adapted geometries based on the digitization of fresh food is proposed in this work. This study is based on the application of advanced technologies for the design and development of food packaging, in this case apples, in order to improve the quality of the packaging. The results obtained show that it is possible to use advanced technologies in the early stages of product design in order to obtain competitive products adapted to new emerging needs.

Low-cost RGB-D cameras are increasingly used in several research fields including human-machine interaction, safety, robotics, biomedical engineering and even Reverse Engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras proved to be among the best suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e. ~160-10000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations on the RealSense D415. In particular, tests are carried out to assess the device performances in the near range (i.e. 100-1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e. the German VDI/VDE 2634 part 2 normative) with a number of literature-based strategies. Performance analysis is finally compared against latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in Reverse Engineering applications.

The use of high strength to weight ratio laminated composites is emerging in marine industry and applications as a very efficient solution for improving productivity. Nevertheless, delamination between the layers is a limiting factor for the wider application of laminated composites, as it reduces the stiffness and strengths of the structure. Interleaving nanofibrous mats between layers of composite laminates has been proved to be an effective method for improving composites delamination resistance. This paper aims to characterize the effect of interleaved nanofiber on mode I interlaminar properties and failure mechanisms when subjected to static and fatigue loadings. For this purpose, virgin and nanomodified woven laminates were subjected to Double Cantilever Beam (DCB) specimens. Static and fatigue tests were performed and the tests were monitored by acoustic emission technique. The mechanical results showed a 130% increase of delamination toughness for nanomodified specimens in the static loadings and more crack growth resistance in the fatigue loading. The AE results revealed that different type of failure mechanisms was the cause of these improvements for the modified specimens compared with the virgin ones.

Facing with errors of a product after machining was a big title in this field. That will affect to product’s quality with unpredictable situation when products is in use for some particular application such as in medical facilities. Improving the precision of machine tools by exploring temperature on multiple points and measuring differential locations of the spindle is going to be executed in this study. A temperature measurement tool and a software running on Windows platform have been developed and combining with Non-bar system in order to support analyzing the temperature rising with the changes of length of the spindle to find the compensation solution based on eccentricity and length distortion. In this study, the whole tests have done on UX300 five – axis CNC machine tools for this investigation and the errors after applying compensation function have reduced 70% at least, with respect to the errors after machining without using compensation function.

This report describes the collection of a large dataset (6930 measurement) on dimensional error in the fused deposition modeling (FDM) additive manufacturing process for full-density parts. Three different print orientations were studied, as well as seven raster angles (0°, 15°, 30°, 45°, 60°, 75°, and 90°) for the rectilinear infill pattern. All measurements were replicated ten times on ten different samples to ensure a comprehensive dataset. Eleven polymer materials were considered: acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), high-temperature PLA, wood-composite PLA, carbon-fiber-composite PLA, copper-composite PLA, aluminum-composite PLA, high-impact polystyrene (HIPS), polyethylene terephthalate glycol-enhanced (PETG), polycarbonate, and synthetic polyamide (nylon). The samples were ASTM-standard impact testing samples, since this geometry allows the measurement of error on three different scales; the nominal dimensions were 3.25mm thick, 63.5mm long, and 12.7mm wide. This dataset is intended to give engineers and product designers a benchmark for judging the accuracy and repeatability of the FDM process for use in manufacturing of end-user products.

This work studied the corrosion of welded pipes and how welding destroyed surface film of pipes. Surface reaction of a welded pipe is key to understanding phenomena and important factors during the corrosion. This paper presents experiment and CFD modeling approaches of a welded pipe corrosion under salt vapor condition. The pipes were welded at currents of 60 A,70 A and 80 A to observe the effect of welding current on corrosion. A welded pipe is a stainless-steel ASTM A312 grade 304L and period of experiment about 0-600 hours that they are tested in vertical and horizontal alignments. In CFD software, there is not direct model of corrosion but it can use surface reaction and create add-on species and chemical reaction technique for imitating the corrosion mechanism. The modeling approaches of corrosion have presented in 3-dimensional transient times in CFD simulation. Surface reactions were performed by Species Model which involve site species. Site species in Species Model took place at gas-solid interfaces and in this case are salt vapor and surface pipe. Chemical reaction rate on the surface controls lost weight of a welded pipe and the model can be validated with experiment. In conclusion, in period 0-600 hours error between CFD modeling and experiment have error trend decreased. The error at 600 hours is 6% both of vertical pipe and horizontal pipe test. The modeling approaches closely with the performed experiment and can be accepted. Moreover, the model is able to predict corrosion of a welded pipe of different sizes and their lost weight after 600 hours without experiment. Also the model can predict lifetime of pipe.

Face milling is a well known commercial process highly used in heavy industries that consumes high amount of power. Besides power issue, modern manufacturing industries are aiming for per part cost reduction keeping the product quality unimpaired. Unexpectedly if the part is rejected in any stage of manufacturing, the cost of manufacturing dramatically increases. Major cause of part rejection is excessive tool wear that imparts poor surface profile or catastrophic tool failure that causes adherence of broken tool debris onto machined surface. Furthermore, the tool wear is associated with sliding distance (frictional distance) and the tool life quantifies the cost of tools. As such, from the perspective of manufacturing industries it is imperative to optimize the surface quality parameter, cost of part, power consumption, and material removal – this is exactly what is accomplished here. By this work, it is possible to conserve power consumption, produce parts with lower cost, manufacture with uncompromising surface quality and enhanced material removal rate. Moreover, as intermediate factors of interest, the influences of sliding distance, tool life and tool flank wear on the overall machining performance are evaluated. The multi-objective optimization by Grey Relational Analysis (GRA) revealed that for improved product performance and fast manufacturing (case 1) optimum results are: feed per tooth fz = 0.25 mm/tooth, cutting speed vc = 392.6 m/min and cutting length l = 0.5 mm; for resource conservation (case 2) the optimum results are: feed per tooth fz = 0.125 mm/tooth, cutting speed vc = 392.6 m/min, cutting length l = 0.5 mm.

As an essential synthetic material used in continuous casting of steels, mold fluxes improve the surface quality of steel slabs. In this study, a CaO-SiO₂-Na₂O based low-fluorine mold flux was solidified by an improved water-cooled copper probe with different temperatures of molten flux and different probe immersion times. The heat flux through solid films and the film structures were calculated and inspected, respectively. The results indicate: large internal cracks (formed in the glassy layer of films during solidification) were observed, the formation and evolution of those cracks contributed to the the unstable heat flux density. The roughness of the surface in contacted with the water-cooled copper probe formed as films were still glassy and the roughness have no causal relationship with crystallization or devitrification. Combeite with columnar and faceted dendritic shapes is the main crystal in the film.

Machines for direct digital inkjet printing on cylindrical containers are a new technology out on the market. Their commercialization in the industrial sector has been affected by their high precision. This leads to the use of mechanisms with narrow manufacturing tolerances and to the search for topologies that have the least accumulated error without affecting quality. Machines with topologies to work on flat substrates have printing and productivity problems working on cylindrical substrates. This research paper presents the qualitative design of a direct digital inkjet printer working over cylindrical substrates comparing five mechanical topologies; three topologies with radial distribution and two topologies with parallel distribution. The aim of these topologies is to find the precision, quality and efficiency of the printer taking into account the restrictions present in its construction. Each topology has separate constitutive mechanisms, it is analyzed the tolerance ranges between the print head and the substrate whose cumulative error maximizes the inkjet print resolution to determine precision. From five Topologies, number 1, 2 and 5 meet the requirements. the topology 2 meets the requirements but it is not able to be developed due to current technological limitations.

The hybrid personal cooling system (HPCS) consisted of ventilation fans and phase change materials (PCMs) covered with insulation pads is a promising wearable cooling system to mitigate heat strain and heat-related illnesses of occupational workers with heavy labor in hot environments. Effects of clothing characteristics (e.g., thermal resistance of insulation pads, latent heat and melting temperature of PCMs) on the thermal performance of the HPCS have been investigated in detail in our previous study. Apart from the aforementioned factors, environmental conditions, i.e., environmental temperature and relative humidity, also significantly affect the thermal performance of the HPCS. In this paper, a numerical parametric study was performed to investigate the effects of the environmental temperature and relative humidity (RH) on the thermal management of the HPCS. Five levels of air temperature under environmental RH=50% were chosen (i.e., 32, 34, 36, 38 and 40 ºC) to study the impact of environmental temperature on the HPCS’s cooling performance. In addition, four levels of environmental RH at ambient temperatures of 36 and 40 ºC were selected (i.e., 30, 50, 70 and 90%) to examine the effect of RH on cooling performance of the HPCS. Results show that high environmental temperatures could accelerate the PCM melting process and thereby weaken the cooling performance of HPCS. In the moderately hot environment (36 °C), the HPCS presented good cooling performance with the maximum core temperature at around 37.5 °C during excise when the ambient RH≤70%, whereas good cooling performance could be only seen under RH≤50% in the extremely hot environment (40 °C). Thus, it may be concluded that the maximum environmental RH for the HPCS exhibiting good cooling performance decreases with the increase in the environmental temperature.

Many manufacturing sectors require high surface finishing. After machining operations such as milling or drilling, undesirable burrs or insufficient edge finishing may be generated. For decades, many finishing processes have been handmade-basis; this fact is accentuated when dealing with complex geometries especially for high value-added parts. In recent years, it’s a tendency of trying to automate as far as possible this kind of processes, repeatability and time/money savings are main purposes. Based on that idea, the aim of this work is to check new tools and strategies for finishing aeronautical parts, especially critical engine parts made on Inconel 718, a very ductile nickel alloy. Automating edge finishing of chamfered holes is a complicated but really important goal. In this paper, flexible abrasive tools were used for this purpose. A complete study of different abrasive possibilities was carried out, mainly focusing on roughness analysis and final edge results obtained.

The incorporation of plastic matrix composite materials into structural elements of the aeronautical industry requires contour machining and drilling processes along with metallic materials prior to final assembly operations. These operations are usually performed using conventional techniques, but they present problems derived from the nature of each material that avoid implementing One Shot Drilling strategies that work separately. In this work, the study focuses on the evaluation of the feasibility of Abrasive Waterjet Machining (AWJM) as a substitute for conventional drilling for stacks formed of Carbon Fiber Reinforced Plastic (CFRP) and aluminum alloy UNS A97050 through the study of the influence of abrasive mass flow rate, traverse feed rate and water pressure in straight cuts and drills. For the evaluation of the straight cuts, Stereoscopic Optical Microscopy (SOM) and Scanning Electron Microscopy (SEM) techniques are used inspection techniques have been used. In addition, the kerf taper through the proposal of a new method and the surface quality in different cutting regions have been evaluated. For the study of holes, the macrogeometric deviations of roundness, cylindricity and straightness have been evaluated. Thus, this experimental procedure reveals the conditions that minimize deviations, defects, and damage in straight cuts and holes obtained by AWJM.

The optimization of the production process of metal mines has been traditionally driven only by economic benefits while ignoring resource efficiency. However, it has become increasingly aware of the importance of resource efficiency since mineral resource reserves continue to decrease while the demand continues to grow. To better utilize the mineral resources for sustainable development, this paper proposes a multi-objective optimization model of the production process of metal mines considering both economic benefits and resource efficiency. Specifically, the goals of the proposed model are to maximize the profit and resource utilization rate. Then, the fast and elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to optimize the multi-objective optimization model. The proposed model has been applied to the optimization of the production process of a stage in the Huogeqi Copper Mine. The optimization results provide a set of Pareto-optimal solutions that can meet varying needs of decision makers. Moreover, compared with those of the current production indicators, the profit and resource utilization rate of some points in the optimization results can increase respectively by 2.99% and 2.64%. Additionally, the effects of the decision variables (geological cut-off grade, minimum industrial grade and loss ratio) on objective functions (profit and resource utilization rate) were discussed using variance analysis. The sensitivities of the Pareto-optimal solutions to the unit copper concentrate price were studied. The results show that the Pareto-optimal solutions at higher profits (with lower resource utilization rates) are more sensitive to the unit copper concentrate prices than those obtained in regions with lower profits.

Retail buildings can provide energy flexibility to the grid with the possibility of load shifting and building automation systems. Demand response is a collective innovation in the smart grid domain. Various stakeholders should be involved in the demand response activities to ensure the success. The owners or senior management of retail buildings need to consider the stakeholders who are directly influenced by the demand response participation, e.g. customers and employees. Meanwhile, demand response activities are influenced by various factors, such as energy market structure, policy, etc. Therefore, this paper investigates the demand response readiness for retail buildings with three aspects: energy control preferences, stakeholder engagement, and cross-national differences. A questionnaire is designed and collected with store managers in Denmark (N=51) and the Philippines (N=36). The result shows that: 1) retail stores are much readier to participate in the implicit demand response by manual energy control compared to the utility control or building automation. Meanwhile, store managers have significant concerns about business activities and indoor lighting compared to other aspects; 2) the statistically significant influential factors for retail stores to participate in the demand response are related to whether the DR participation matches the company goals, influences business operation, and whether retail stores are lack of related knowledge; 3) retail stores believe that stakeholders should be informed about the DR activities but not involved in; 4) there are significant differences regarding the energy control preferences and concerns between retail stores in Denmark and the Philippines, but no significant difference regarding the stakeholder engagement.

Hybrid machine tools combining additive and subtractive processes have arisen as a solution to the increasing manufacture requirements, boosting the potentials of both technologies, while compensating and minimizing their limitations. Nevertheless, the idea of hybrid machines is relatively new and there is a notable lack of knowledge in the field. Therefore, in the present paper, an insight into the advancements of hybrid machines is given, identifying their real capabilities, together with the latest developments from an industrial context. In addition, the current situation and future perspectives of hybrid machines from the point of view of process planning, monitoring and inspection are discussed. Finally, the challenges that must be overcome and the opportunities that the hybrid machines will provide in the forthcoming years are presented.

In this paper, the fatigue response of Fused Filament Fabrication (FFF) Acrylonitrile butadiene styrene (ABS) parts is studied. Different building parameters (layer height, nozzle diameter, infill density, and printing speed) were chosen to study their influence on the lifespan of cylindrical specimens according to a design of experiments (DOE) using the Taguchi methodology. The same DOE was applied on two different specimen sets using two different infill patterns: rectilinear and honeycomb. The results show that infill density is the most important parameter for both studied patterns. The specimens manufactured with the honeycomb pattern show longer lifespans. The best parameter set associated to that infill was chosen for a second experimental phase, in which the specimens were tested under different maximum bending stresses to construct the Wöhler curve associated to this 3D printing configuration. The results of this study are useful to design and manufacture ABS end-use parts that are expected to work under oscillating periodic loads.

The present work aims at the comprehensive application of stochastic and optimization tools with the support of Information and Communication Technologies (ICT) through a case study in a logistics process for electronic goods; simulation and Response Surface Methodology (RSM) are applied for this purpose. The problem to be evaluated is to define an optimal distribution cost for products shipped to wholesale customers located in different cities in Mexico from a manufacturing plant in Tijuana, Mexico. The factors under study are the product allocation for each distribution center, finished good inventory level and on time deliveries, which are supposed to be significant to get the objective. The methodology applied for this problem considers the design of a discrete event simulation model to represent virtually the real life of logistics process, which is considered a complex system due to different activities are interrelated to carry it out. This model is used to execute the different experiments proposed by the RSM. The results obtained from simulation model were analyzed with the RSM to define the mathematical model that allows identifying the parameters of the factors in order to optimize the process. The findings prove how the ICT facilitate the application of stochastic tools with the purpose of process optimization.

Sustainability in material purchasing is a growing area of research. Goods purchasing decisions strongly affect transportation path flows, vehicle consolidation, inventory levels and related obsolescence costs. Within a global sourcing context, companies experience the need of new decision making approaches capable to consider a large variety of factors, also linked with society and environment. Environmental impact assessment has become a key requirement for materials purchasing and transportation decisions since global warming is a rising concern both in academic and industrial researches. In fact, it is well known that the freight transport industry is responsible for large amounts of carbon emissions contributing to global warming. In this paper, we initially analyse and compare the environmental economic policies established by the International Governments in relation to the carbon trading systems adopted. Then, we develop a multi-objective lot sizing approach useful in practice to define the sustainable quantity to purchase when a Cap and Trade mitigation policy is present. We further analyse the model behaviour according to different carbon price values by demonstrating that carbon prices are still far too low to motivate managers towards sustainable purchasing choices.

Although distributed additive manufacturing can provide high returns on investment the current markup on commercial filament over base polymers limits deployment. These cost barriers can be surmounted by eliminating the entire process of fusing filament by 3-D printing products directly from polymer granules. Fused granular fabrication (FGF) (or fused particle fabrication (FPF)) is being held back in part by the accessibility of low-cost pelletizers and choppers. An open-source 3-D printable invention disclosed here provides for precise controlled pelletizing of both single thermopolymers as well as composites for 3-D printing. The system is designed, built and tested for its ability to provide high tolerance thermopolymer pellets from a number of sizes capable of being used in a FGF printer. In addition, the chopping pelletizer is tested for its ability to chop multi-materials simultaneously for color mixing and composite fabrication as well as precise fractional measuring back to filament. The US$185 open-source 3-D printable pelletizer chopper system was successfully fabricated and has a 0.5 kg/hr throughput with one motor, and 1.0 kg/hr throughput with two motors using only 0.24 kWh/kg during the chopping process. Pellets were successfully printed directly via FGF and indirectly after being converted into high-tolerance filament in a recyclebot.

The use of commodity polymers such as polypropylene (PP) is key to open new market segments and applications for the additive manufacturing industry. Technologies such as powder-bed fusion (PBF) can process PP powder; however, much is still to learn concerning process parameters for reliable manufacturing. This study focusses in the process-property relationships of PP using laser-based PBF. The research presents an overview of the intrinsic and the extrinsic characteristic of a commercial PP powder as well as fabrication of tensile specimens with varying process parameters to characterize tensile, elongation at break, and porosity properties. The impact of key process parameters, such as power and scanning speed are systematically modified in a controlled design of experiment. The results were compared to the existing body of knowledge; the outcome is to present a process window and optimal process parameters for industrial use of PP. The computer tomography data revealed a highly porous structure inside specimens ranging between 8.46% and 10.08%, with porosity concentrated in the interlayer planes in the build direction. The results of the design of experiment for this commercial material show a narrow window of 0.122 ≥ Ev ≥ 0.138 J/mm³ led to increased mechanical properties while maintaining geometrical stability.

This paper provides an overview of the new CPAM Project on Additive Manufacturing (AM) in design and simulation, focusing on topology & lattice structure optimization for a light-weighting advantage. This industry/academia collaboration project aims to utilize existing hardware and software tools, and investigate the practical limits of the technologies, providing eventual guidelines for general use. This will provide a solid foundation for the practical use of metal AM optimized solid and latticed structures especially for Ti6Al4V parts. Two case studies are demonstrated here, one a purely topology optimized design, and one also incorporating lattice optimized design, both from Ti6Al4V and load-bearing components, to be utilized in the Nelson Mandela University (NMU) Eco-Car Project in competition, late in 2018. This paper presents the Design for Additive Manufacturing (DfAM) process, the challenges met iro applying a DfAM design mindset, and a unique final voxel-based smoothing step finishing off the design process. Detailed structural integrity assessment of these parts are included - the question remains: can Additive Manufacturing help win the race?

Due to the upward trend in the globalization of sustainability issues and the intense competitive environment, it is evident that higher education institutions need new strategic approaches to succeed. To this end, the inquiry for this paper has been made into the debate about student relationship management. Going through the literature indicates that institutions have mainly perceived the concept as a technological initiative for solving the problems in individual domains, accompanied by uncoordinated efforts. Thus, the aims of this study are to theoretically present critical success factors of this strategic approach and to empirically examine the recognized factors. To do so, confirmatory factor analysis that is a quantitative analytic method was performed. The results and analyses revealed that there has been a significant correlation between the four critical success factors including knowledge management, student relationship management technology, student orientation, and employees’ involvement. It was also found that these factors are significantly correlated with the construct of student relationship management success. The findings have consequently highlighted that in addition to the technological tool, the role of knowledge management, employees’ involvement, and student orientation appeared to be particularly important for the implementation of the application.