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.

Electromobility promises to efficiently mitigate consequences of increasing traffic volume and its accompanied greenhouse gas emissions. On an individual level, electrified bikes allow emission free electrified mobility at moderate costs and consequently their stock has increased significantly in recent years. This simultaneously increases the demand for spare parts, which are often manufacturer or application-specific, and due to many variants, challenging to provide for the market. This article evaluates powder-based and extrusion-based metal additive manufacturing of a typical electrified bike component to demonstrate an alternative spare parts supply. The investigation demonstrates how these parts can be additively manufactured function equivalent and with sufficient mechanical properties, also taking economical aspects into account. Furthermore, the needed resources and related environmental consequences for metal-based additive manufacturing spare-part production are compared for both process routes. The results show that both routes are capable of producing spare-parts at comparatively same mechanical performance and resource costs, while needed resources such as energy, gases and manufacturing time are significantly lower for powder-based, respectively machine costs for extrusion-based additive manufacturing. Therefore, additive manufacturing offers a promising opportunity to produce parts in small quantities resource efficient and rapidly.

Metal Additive Manufacturing (MAM) has emerged as a promising technology in the aerospace industry, enabling the production of complex components with enhanced design flexibility and reduced lead times. Concurrently, Industry 4.0 has gained significant attention for its potential to revolutionize aerospace manufacturing processes. The paper aims to cluster and review the literature on MAM in the aerospace industry, specifically focusing on its relevance to Industry 4.0 and its applicability throughout the various stages of the product life cycle for the chief purpose of matching supply with demand. Identified gaps and challenges are analyzed, highlighting the need for further research and outlining research agenda aimed at advancing the integration of MAM for Industry 4.0 in the aerospace sector. By addressing these research areas, the aerospace industry can unlock the value of MAM within an Industry 4.0 framework, leading to improved efficiency, productivity, and competitiveness.

Remanufacturing is a way for product recovery initiative and maintaining sufficient production flow to satisfy customer demand through circular economy. Recently, manufacturers have been progressively incorporating remanufacturing processes, making their supply chain vulnerable to disruptions. Among the disruptions that usually occur is the shortage in spare parts supply, which causes unexpected delay in production and possible loss of sales. In the face of potential disruption, it is essential for remanufacturing facilities to manage their supply chains in an optimum manner as to decrease the adverse impact of disruptions to their business. In this study, a two-stage production-inventory system is analysed by developing a cost-minimisation model focusing on the recovery schedule after a disruption occurrence in sourcing spare parts for a remanufacturer’s production cycle. The developed model was solved using the branch and bound algorithm. Through numerical experiments, the results indicate that the optimal recovery schedule and the number of recovery cycles are significantly dependant on disruption time, lost sales and backorder costs. Moreover, a sensitivity analysis shows that the lost sales option seems to be more effective than the backorder option in optimising the system’s overall cost. The proposed model could assist managers in deciding the optimal production strategy while providing interesting managerial insights into vital spare parts recovery issues when disruption strikes.

The promised dataset was obtained from the daily Turkish words and phrases pronounced by various people in the videos posted on YouTube. The purpose of collecting the dataset is to provide detection of the spoken word by recognizing patterns or classifying lip movements with supervised, unsupervised, semi-supervised learning and machine learning algorithms. Most of the datasets related with lip reading consist of people recorded on camera with fixed backgrounds and the same conditions, but the dataset presented here consists of images compatible with machine learning models developed for real-life challenges. It contains a total of 2335 instances taken from TV series, movies, vlogs, and song clips on YouTube. The images in the dataset vary due to factors such as the way people say words, accent, speaking rate, gender and age. Furthermore, the instances in the dataset consist of videos with different angles, shadows, resolution, and brightness that are not created manually. The most important feature of our lip reading dataset is that we contribute to the non-synthetic Turkish dataset pool, which does not have wide dataset varieties. Machine learning studies can be carried out in many areas, such as the defense industry and social life, with this dataset.

This study focuses on understanding how global auto parts companies manage innovation by analyzing keywords in CEO messages from their reports. The study involved three key phases: (1) extraction of innovation-related keywords emphasized in CEO messages, (2) comparison of innovation-related keyword usage between high-performing (upper group) and low-performing (lower group) companies, and (3) analysis of the characteristics of innovation management via internal meaning and the structural interpretation of keywords. A comparison between the upper and lower groups revealed distinct differences in the emphasized keywords. The mapping of open innovation success factors, based on keywords extracted according to eigenvector centrality, identified Provision of Resources and Governance as central to innovation management in the upper group. These findings serve as a benchmark for establishing the direction, vision, and strategy of innovation management in the lower group.

We designed a novel growth controller regulated by feeding of an unnatural amino acid, Nε-benzyloxycarbonyl-L-lysine (ZK), using a specific incorporation system at a sense codon. This system is constructed by a pair of modified pyrrolisyl-tRNA synthetase (PylRS) and its cognate tRNA (tRNApyl). Although ZK is non-toxic for normal organisms, the growth of Escherichia coli carrying the ZK incorporation system was inhibited in a ZK concentration-dependent manner without causing rapid bacterial death, presumably due to generation of non-functional or toxic proteins. The extent of growth inhibition strongly depended on the anticodon sequence of the tRNApyl gene. Taking advantage of the low selectivity of PylRS for tRNApyl anticodons, we experimentally determined the most effective anticodon sequence among all 64 nucleotide sequences in the anticodon region of tRNApyl gene. The results suggest that the ZK-regulated growth controller is a simple, target-specific, environmental noise-resistant and titratable system. This technique may be applicable to a wide variety of organisms because the growth inhibitory effects are caused by “informational disturbance”, in which the highly conserved system for transmission of information from DNA to proteins is perturbed.

Additive manufacturing (AM) provides customization of the microstructure and mechanical properties of components. Selective laser melting (SLM) is the commonly used technique for processing high strength Aluminum alloys. Selection of SLM process parameters could control the microstructure of fabricated parts and their mechanical properties. However, process parameter limits and defects inside the as-built parts present obstacles to customized part production. This study is the second part of a comprehensive work that investigates the influence of SLM process parameters on the quality of as-built Al6061 and AlSi10Mg parts. The microstructure of both materials was characterized for different parts processed over a wide range of SLM process parameters. The optimized SLM parameters were investigated to eliminate the internal microstructure defects. Mechanical properties of the parts were illustrated by regression models generated with design of experiment (DOE) analysis. The results reported in this study were compared to previous studies, illustrating how the process parameters and powder characteristics could affect the quality of produced parts.

Additive manufacturing (AM) offers customization of microstructure and mechanical properties of fabricated components according to the material selected, and process parameters applied. Selective laser melting (SLM) is the commonly used technique for processing high strength aluminum alloys. Selection of SLM process parameters could control the microstructure of parts and their mechanical properties. However, the process parameters limit and defects obtained inside the as-built parts present obstacles to customized part production. This study investigates the influence of SLM process parameters on the quality of as-built Al6061 and AlSi10Mg parts according to the mutual connection between the microstructure characteristics and mechanical properties. The microstructure of both materials was characterized for different parts processed over a wide range of SLM process parameters. The optimized SLM parameters were investigated to eliminate the internal microstructure defects. The behaviour of mechanical properties of parts was presented through regression models generated from the design of experiment (DOE) analysis for the results of hardness, ultimate tensile strength, and yield strength. A comparison between the results obtained and that reported in the literature is presented to illustrate the influence of process parameters, build environment, and powder characteristics on the quality of parts produced. The results obtained from this study could help to customize the part’s quality by satisfying their design requirements in addition to reducing the as-built defects which in turn reduce the amount of the post-processing needed.

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

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.

This paper examines some of the largely neglected areas of opportunity to utilize skeletal space structure systems in support of modular, industrialized, economical, sustainable and digital future of the construction industry. In this context, the feasibility of future use of skeletal space structures is studied for a few classes of engineering structures, namely, residential apartment buildings, and offshore platforms, along with their suitability for reconstruction, renovation, modernization and retrofit of damaged buildings and urban areas of cultural heritage significance. Finally, the particulars of lean project management of space structures are discussed with emphasis on engineering and economic factors, production management, environmental aspects, quality management, and reliability, maintainability and sustainability considerations. This article concludes that skeletal space structures can fulfil many of the essential construction requirements of modern societies, especially those facing environmental challenges, all while allowing for design flexibility and mass customization.

The results of physico-chemical studies of individual fruit parts, including crusts, partitions, juice and seeds themselves, extracted from immature (green), unripe and mature fruits of three harvest periods are presented: 20.VII, 01.IX and 15.X. The analyses were carried out in 2019-20 during the period of active growth and ripening of fruits in the varieties Spring, Iridanaly, Guleisha pink and wild pomegranate from the Geokchay region (Azerbaijan) using standard and generally accepted physico-chemical methods. The ripe fruit (130-288 g) consisted of 60-113% of the peel (peel together with partitions), 53-140% of the juice and 17-85% of the seeds themselves. The edible part of the ripened fruits (53.85-73.2 of the total weight of the fruit) consisted of 58.54-78.65% juice and 21.35 -41.46% seeds, fresh juice contained 15.7-17.7% solids, including 12.04-13.84% sugar, 0.17-0.30% protein, 0.15—0.23% pectin, 0.30-0.40% ash and 0.26-0.57% total polyphenols. The concentrations of solids, protein, ash and total polyphenols in the seeds themselves, remaining after the extraction of juice from the grains of ripe fruits, were 50.0-58.2, 4.13-5.75, 0.60-1.0 and 0.08-0.32%, respectively. There were fewer total polyphenols, flavonoids, chlorogenic acids, catechins and leucoanthocyanins in ripe fruits, and more ascorbic acid, anthocyanins and proanthocyanidins than in unripe ones. The period from the second half of July to the beginning of September is the time of a real “boom” of common polyphenols.