Abstract: Profile conveyor belts are used in operational applications where the transport of bulk materials is required at high inclinations of conveyor belts, typically in the range of 30-40°. The paper deals with the analytical determination of the critical angle of inclination of a homogeneous transverse profile (protrusion), beyond which relative movement of bulk material occurs on the surface of the conveyor belt. The compressive forces induced by the known gravity component of the bulk material acting on a 20 mm high transverse protrusion were experimentally measured on a specially designed laboratory apparatus. The measurements were performed at different inclination angles of the folding plate, which simulated the working surface of the conveyor belt. During the experiments, the investigated bulk material - river gravel with a grain size of 48 mm - was placed in a plastic frame with a width corresponding to the defined loading width of the conveyor belt. On the basis of the measured values of compressive forces, the static coefficient of shear friction in contact of grains of bulk material with two types of surfaces, namely plastic and rubber, was analytically determined. From the experimental data, the mean values of the static shear friction coefficient were determined, which were 0.33 for the plastic surface and 0.48 for the rubber surface, with the orientation of the protrusion perpendicular (90 deg) to the longitudinal axis of the conveyor belt. The experimental investigation also included the determination of the internal friction angle of the river gravel. The results show that when bulk material is conveyed by a profile conveyor belt, it is possible to safely convey material with a cross-sectional height greater than the height of the transverse protrusion, provided that the conveyor inclination angle does not exceed the internal friction angle of the bulk material.

Abstract: The concept of maintenance has undergone a significant evolution, adapting to the changing demands of industry over time. Initially limited to corrective actions during the Industrial Revolution—often performed without specialized personnel or dedicat-ed departments—modern maintenance now incorporates advanced design considera-tions such as reliability, maintainability, safety, sustainability, and performance. This research presents a novel methodology aimed at integrating maintainability into the early stages of equipment and system design. Centered on continuous improvement, the approach prioritizes design variables that facilitate efficient maintenance throughout the asset’s lifecycle. Grounded in the UNE 151001 standard and employing the Quality Function Deployment (QFD) technique, the proposed methodology intro-duces the “House of Maintainability”—a structured tool that supports maintainabil-ity-oriented design and allows for diagnostic assessments of existing systems. By cap-turing stakeholder requirements and maintenance experience across various systems and contexts, the tool systematically translates these inputs into design criteria, ensur-ing compliance with maintainability standards. The methodology is validated through a real-world case study, confirming its practical applicability and effectiveness in en-hancing industrial design processes with a focus on maintainability.

Abstract: Fluid jet polishing process (FJP) demonstrates high shape accuracy and surface quality in the machining of nonlinear and complex surfaces, and it achieves precise and adjustable material removal rates through computer control. However, there are still challenges in terms of machining efficiency, system complexity, and stability. Particularly, there is uncertainty in process optimization, especially with higher challenges in optimizing process parameters after changes in working conditions. This study utilizes digital twin technology to propose a new framework for optimizing the FJP process. By reviewing the application of DT in the machining field, this paper identifies the limitations of existing methods and proposes a human-centric design approach that integrates key factors of DT-driven FJP, such as jet kinetic energy, nozzle structure, abrasive type, and machining path. This method encompasses multiple aspects from removal function models to machining path algorithms. By introducing a core method based on transfer learning, this research aims to improve the predictive accuracy, machining efficiency, and stability of the FJP process, realizing efficient and precise polishing operations. Ultimately, this paper validates the proposed method through a case study on 3D printed workpieces, discusses the key enabling technologies, and main challenges. This study not only advances the application potential of FJP process but also provides a new perspective and strategy for optimizing complex machining processes using DT technology.

Abstract: Si MOSFETs are widely used in power conversion systems; however, long-term operation under repetitive switching and electro-thermal stress leads to progressive degradation and eventual failure. Two representative failure modes are commonly observed: gate-oxide degradation and packaging-related degradation, which often exhibit different evolution patterns. This paper proposes an AI-based diagnosis and prognostics framework that jointly leverages steady-state time-series information and fixed-length features extracted from turn-off transients. The study utilizes the NASA Open Accelerated-Aging dataset and reorganized/preprocessed data supported by MATLAB/Simulink measurement cir-cuit modeling. Physics-informed rule-based labeling is applied to discriminate normal, gate-oxide, and packaging-related conditions based on degradation indicators such as R_{ds_on} evolution. The trained model is further interpreted via permutation importance to quantify whether gradual/abrupt degradation indicators and transient features contribute to decision-making. Performance is assessed on held-out tests and synthesized cases sampled from baseline operating distributions to examine consistency under previously unseen conditions.

Abstract: Manual assembly of multi-pin cable harnesses remains vulnerable to miswiring when conductors are visually indistinguishable. This paper presents an industrial case study of a quick-connect harness composed of two connectors (receptacle-type and pin-type) linked by 16 black conductors (2.5 mm²; 200 mm length), where the dominant failure mode is a two-wire swap that breaks correct pin-to-pin mapping and may cause downstream equipment damage. In the baseline state, end-of-line verification relied on visual inspection only (1 min/unit), resulting in an internal nonconformity rate of 4% (repairable). To achieve the operational goal of zero defects (zero escapes), we propose and integrate an electronic pin-to-pin continuity and mapping fixture as a deterministic End-of-Line (EOL) quality gate implementing poka-yoke logic (“no PASS—no shipment”) and enabling structured traceability records. Using a before–after workload model that includes mandatory retest after rework, the fixture reduces test time to 0.33 min/unit. For a monthly volume of 1500 units, total quality workload (test + rework + retest) decreases from 31 h/month to 13.58 h/month, releasing 17.42 h/month. Global quality productivity increases from 48.39 units/h to 110.46 units/h (+128%). The proposed architecture couples deterministic electrical verification with data logging aligned to digital thread and data-driven quality management concepts to sustain continuous improvement and prevent customer escapes.

Abstract: This paper presents a Load-Dependent Multimodal Vibration Signal Enhancement and Fusion Framework (LD-MVSEFF) for load-specific condition monitoring, building on the Customised Load Adaptive Framework (CLAF). The proposed approach enhances the classification of CLAF load-dependent fault subclasses namely Healthy, Mild, Moderate, and Severe by integrating complementary information from raw vibration signals and signal-encoded representations. Three input channels are employed, combining time–frequency domain features with Continuous Wavelet Transform (CWT) and Gramian Angular Difference Field (GADF) image encodings, with each channel independently trained and evaluated to identify its most effective classifiers. To address the reduced separability of the Mild and Moderate fault subclasses under varying load conditions, a weighted decision fusion strategy is introduced, assigning classifier contributions according to their class-specific strengths. Experimental evaluation over five runs demonstrates high and stable performance, with the best configuration achieving an overall accuracy of 99.04% ± 0.22% and an average training time of 18 min and 30 s. The results confirm the effectiveness of LD-MVSEFF as a robust multimodal methodology for load-specific condition monitoring.

Abstract: Visual–inertial attitude estimation systems often suffer from accuracy degradation and instability when visual measurements are intermittently lost due to occlusion or illumination changes. To address this issue, this paper proposes an LSTM-EKF framework for dynamic attitude estimation under visual information loss. In the proposed method, an LSTM-based vision prediction network is designed to learn the temporal evolution of visual attitude measurements and to provide reliable pseudo-observations when camera data are unavailable, thereby maintaining continuous EKF updates. The algorithm is validated through turntable experiments, including long-term reciprocating rotation tests, continuous visual occlusion scanning experiments, and attitude accuracy evaluation experiments over an extended angular range. Experimental results show that the proposed LSTM-EKF effectively suppresses IMU error accumulation during visual outages and achieves lower RMSE compared with conventional EKF and AKF methods. In particular, the LSTM-EKF maintains stable estimation performance under a certain degree of visual occlusion and extends the effective attitude measurement range beyond the camera’s observable limits. These results demonstrate that the proposed method improves robustness and accuracy of visual–inertial attitude estimation in environments with intermittent visual degradation.

Abstract: This paper develops a generative AI decision-support and optimisation framework for advancing sustainability and resilience in industrial logistics. The framework combines data aggregation, generative scenario creation, simulation-based evaluation, and multi-objective optimisation to support evidence-based management under tightening European Union sustainability regulations. Building upon the decision-aid lineage of the International Journal of Production Research, it integrates policy variables such as the Carbon Border Adjustment Mechanism (CBAM), the EU Emissions Trading System for maritime transport, FuelEU Maritime, the Digital Product Passport (DPP), and the Corporate Sustainability Reporting Directive (CSRD) directly into logistics-planning equations. Recent studies on digital twins and adaptive optimisation (Longo et al., 2023; Flores-García et al., 2025) highlight the need for AI systems that translate these policies into dynamic cost and carbon trade-offs. The proposed model responds to this need by coupling generative scenario synthesis with traceable optimisation and governance controls consistent with the EU AI Act (European Commission, 2025). An illustrative case from the mining-rope industry demonstrates how global sourcing and transport routes in European, South African, and Chinese configurations can be simulated within the generative environment to evaluate comparative cost, emission, and compliance profiles. Both SME-light and enterprise implementations achieved reduced analysis time and improved transparency of carbon-related decisions. The study contributes a replicable methodology that transforms generative AI from a creative text tool into a quantifiable governance instrument, linking strategic foresight with operational resilience in sustainable logistics networks.

Abstract: Generative Artificial Intelligence (AI) is transforming quality management (QM) and auditing by expanding automation, supporting data-driven decisions, and enabling more personalized stakeholder interaction. However, its adoption also raises concerns related to system robustness, operational resilience, and regulatory compliance, including potential deviations from Critical-to-Quality (CTQ) requirements, gaps in traceability, and misalignment with established quality standards. This paper proposes a structured conceptual framework for proactive, generative AI-enabled QM and auditing, organized into three functional domains: supplier performance, in-process control, and post-market feedback. The framework shows how generative AI can: 1) strengthen supplier oversight via automated documentation and early risk identification; 2) improve in-process control through real-time anomaly detection and Statistical Process Control (SPC)–based triage; and 3) enhance post-market surveillance using predictive analytics for warranty clustering and prioritized Corrective and Preventive Action (CAPA) preparation. To ensure compliance and auditability, the framework incorporates policy-based constraints, human-in-the-loop checkpoints, and end-to-end digital traceability. Verification was performed through a proof-of-concept case study spanning discrete manufacturing and process-based production environments, comparing a conventional quality workflow with a generative AI-augmented alternative. Expert assessment indicated that the generative AI-assisted workflow achieved better performance on key criteria, including documentation completeness, defect detection, process stability, governance and time efficiency. The obtained results suggest that the proposed framework can support a shift from reactive quality control towards predictive and preventive improvement while preserving alignment with quality standards and organizational quality objectives.

Abstract: Tourism Micro, Small, and Medium Enterprises (MSMEs) in underdeveloped regions play a crucial role in driving local economic development and sustaining the tourism ecosystem. Yet, they face limitations in innovation capacity and organizational performance. This study aims to develop and test a green innovation model to improve MSME organizational performance and strengthen the tourism ecosystem in East Sumba Regency, Indonesia. This study employed a quantitative approach, collecting data through questionnaires from tourism MSMEs, which were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that green innovation, represented by product value, technology, networking, marketing, and market demand, has a positive and significant impact on organizational performance, which, in turn, acts as a key mediator in improving ecosystem performance, as reflected in productivity and resilience. These findings confirm that the impact of green innovation on the tourism ecosystem is indirect and dependent on strengthening the operational and financial performance of MSMEs. The novelty of this study lies in integrating the empirical PLS-SEM model with an implementation approach, including the development of training modules and the digitalization of learning, in the context of 3T regions (Frontier, Outermost, and Underdeveloped). Limitations in this study use data from a single time period; further research is recommended to use multi-period data to capture the dynamics of change better.

Abstract: Maintenance organizations face growing volumes of spare parts, requiring robust classification methodologies to support decision-making. Practitioners continue reliance on simple and single-criterion-specialized methodologies, while research advances toward criteria and threshold specialized classification optimization for operationally visible spare parts or predefined classes revealing criteria dependencies and data completeness requirements. The literature review identifies a gap showing that existing classification methodologies lack inclusion of all spare parts with maintainable asset relevance, consequently excluding, under-prioritizing, or misclassifying essential spare parts leading to the wrong forecasts and inventory policies. Applying design science research, this study develops a holistic spare parts portfolio classification methodology that increases spare parts inclusion and enables class-based decision-making strategy development to address the gap. The methodology classifies spare parts based on their absence and presence across equipment bill of materials, maintenance history, inventory, and inventory policies, enabling identification and inclusion of operationally invisible spare parts. A case study of 32,521 spare parts demonstrates the interventional effects of the methodology. The intervention improved decision-making efficiency by 91%, increased decision throughput ninefold, and transformed a non-transparent decision-making approach with 9% scope completion and 1.7% stock value increase into a transparent strategy-based approach yielding full scope completion and 33.6% scope stock value reduction.

Abstract: Wire Arc Additive Manufacturing (WAAM) is a cost-effective method for fabricating large aluminum components; however, it tends to suffer from heat accumulation and coarse anisotropic microstructures, which can limit the part's performance and its mechanical properties. In this study, a wall is fabricated using a hybrid unified additive deformation manufacturing process (UAMFSP) method, which integrates friction stir processing (FSP) into WAAM, and is compared with a WAAM-only wall fabricated by Metal Inert Gas (MIG) deposition. Based on the outcomes, Infrared (IR) thermography revealed progressive heat buildup in WAAM-only MIG walls, with peak layer temperatures of about 870 to 1000 °C and occasional clipped peaks near the IR-camera limit (~1300 °C). In contrast, in the UAMFSP process, heat was redistributed through mechanical stirring, maintaining more uniform sub-solidus profiles below approximately 400 °C. Also, optical microscopy and quantitative image analysis showed that MIG walls developed coarse, dendritic grains with a mean grain area of about 314 µm², whereas the UAMFSP produced refined, equiaxed grains with a mean grain area of about 10.9 µm², which is approximately 1.5 orders of magnitude smaller. Mechanical performance assessment through microhardness measurement confirmed that the UAMFSP process can improve the hardness by 45.8% compared to the MIG process (75.8 ± 7.7 HV vs. 52.0 ± 1.3 HV; p = 0.0027). In summary, the outcomes of this study introduce the UAMFSP process as a robust method for addressing the thermal and microstructural limitations of WAAM and improving the performance of the fabricated part. By combining deposition with plastic deformation, UAMFSP enables the fabrication of aluminum parts with fine isotropic microstructures and improved strength. These findings provide a framework for further extending hybrid additive-deformation strategies to thicker builds, alternative alloys, and service-relevant mechanical evaluations.

Abstract: This study presents a drone-based method for assessing the condition of road markings from high-resolution imagery acquired by an unmanned aerial vehicle (UAV). A DJI Matrice 300 RTK equipped with a Zenmuse P1 camera is flown over urban road corridors to capture images with centimeter-level ground sampling distance. In contrast to common approaches that rely on vehicle-mounted or street-view cameras, using a UAV reduces survey time and deployment effort while still providing views that are suitable for marking. The flight altitude, overlap, and corridor pattern are chosen to limit occlusions from traffic and building shadows while preserving the resolution required for condition assessment.From these images, the method locates individual markings, assigns a class to each marking, and estimates its level of deterioration. Candidate markings are first detected with YOLOv9 on the UAV imagery. The detections are cropped and segmented, which refines marking boundaries and thin structures. The condition is then estimated at the pixel level by modeling gray-level statistics with kernel density estimation (KDE) and a two-component Gaussian mixture model (GMM) to separate intact and distressed material. Subsequently, we compute a per-instance damage ratio that summarizes the proportion of degraded pixels within each marking. All results are georeferenced to map coordinates using a 3D reference model, allowing visualization on base maps and integration into road asset inventories. Experiments on unseen urban areas report detection performance (precision, recall, mean average precision) and segmentation performance (intersection over union), and analyze the stability of the damage ratio and processing time. The findings indicate that the drone-based method can identify road markings, estimate their condition, and attach each record to geographic space in a way that is useful for inspection scheduling and maintenance planning.

Abstract:

Injection molding processes generate large volumes of heterogeneous process data that reflect complex and dynamic manufacturing conditions, directly influencing product quality. Conventional quality control approaches based on fixed statistical thresholds and operator experience often fail to ensure stable and secure operation under such variability, leading to increased defect rates and reduced process reliability. Moreover, insufficient consideration of data integrity and process stability can compromise the robustness of data-driven manufacturing systems. This study proposes a secure machine learning framework for defect detection and quality enhancement in injection molding processes. The framework integrates systematic data preprocessing, correlation analysis, and an unsupervised learning approach based on a Gaussian Mixture Model (GMM) to achieve reliable defect classification. Key process parameters contributing to quality deviations are identified through statistical correlation analysis, and process data are clustered into one normal group and three abnormal groups corresponding to different defect types without requiring labeled datasets. By emphasizing data integrity, stable clustering behavior, and resilience to process variability, the proposed framework enhances manufacturing security and reduces the risk of misclassification. Experimental results using real injection molding process data demonstrate effective defect reduction, improved process parameter optimization, and enhanced operational efficiency. By enhancing defect detectability and reducing misclassification risks, the proposed framework supports safer and more human-centric manufacturing environments by improving operator trust, decision confidence, and preventive intervention capability.

Abstract: The article considers issues related to improving the surface characteristics of titanium Gr2 using one of the lightest, cheapest and ecological methods - electrospark deposition (ESD) with low pulse energy and with ultradisperse electrodes TiB2-TiAl with nanosized additives of NbC and ZrO2. By means of profilometric, metallographic, XRD, SEM and EDS methods, the change in the geometric characteristics, composition, structure, micro- and nanohardness of the coatings as a function of the electrical parameters of the ESD regime has been studied. The results show that the use of TiB2-TiAl electrodes and low pulse energy allows the formation of dense, continuous and uniform coatings, demonstrating a significant reduction in roughness and inherent irregularities and structural defects of electrospark coatings, as well as the synthesis of newly formed wear-resistant phases and amorphous-nanocrystalline structures. The obtained coatings have crystal-line-amorphous structures, with newly formed intermetallic and wear-resistant phases with minimal defects, with roughness, thickness and microhardness, which can be controlled within the ranges Ra =1.8-3.5 µm, δ= 8-20 µm, HV=9-13 GPa, respectively. Energy pulse parameters have been defined to produce of coatings with a predetermined thickness and roughness, maximum increased hardness and with the most favorable characteristics in terms of abrasive and corrosion resistance.

Abstract: This study investigates critical research gaps in procurement management challenges faced by Chinese contractors in international engineering–procurement–construction (EPC) projects under the Belt and Road Initiative (BRI), with a particular focus on sustainability-oriented outcomes. It examines: (1) prevalent procurement inefficiencies, such as communication delays and material shortages, encountered in international EPC projects; (2) the role of supply chain INTEGRATION in enhancing procurement performance; (3) the application of social network analysis (SNA) to reveal inter-organizational relationships in procurement systems; and (4) the influence of stakeholder collaboration on achieving efficient and sustainable procurement processes. The findings demonstrate that effective supply chain integration significantly improves procurement efficiency, reduces delays, and lowers costs, thereby contributing to more sustainable project delivery. Strong collaboration and transparent communication among key stakeholders—including contractors, suppliers, subcontractors, and designers—are shown to be essential for mitigating procurement risks and supporting resilient supply chain operations. SNA results highlight the critical roles of central stakeholders and their relational structures in optimizing resource allocation and enhancing risk management capabilities. Evidence from case studies further indicates that Chinese contractors increasingly adopt sustainability-oriented practices, such as just-in-time inventory management, strategic supplier relationship management, and digital procurement platforms, to reduce inefficiencies and environmental impacts. Overall, this study underscores that supply chain INTEGRATION, combined with robust stakeholder collaboration, is a key enabler of sustainable procurement and long-term competitiveness for Chinese contractors in the global EPC market.

Abstract: Mechatronics developed under the influence of the Third Industrial Revolution and was a discipline that provided methods and tools for the development of industrial robots, advanced machine tools, mobile phones, and automobiles, among other sophis-ticated products. With the emergence of Industry 4.0 in 2011, mechatronics has be-come indispensable, as traditional production systems are being transformed into cyber-physical systems (CPS), some of which are composed of sophisticated technolo-gies such as Digital Twins (DT) and sophisticated robots, among others. In 2020, the Fifth Industrial Revolution began, giving rise to so-called Human Cyber-Physical Sys-tems and promoting the use of Cobots in industries. Because today's industrial world is influenced by three active industrial revolutions and two transitions, it is possible to find machines and production systems that were designed with different principles and for different purposes, making it necessary to propose a classification that allows each system to be located according to the premises of its respective industrial revolu-tion. This article analyzes the evolution of mechatronics and proposes a classification of machines and production systems based on the premises of each industrial revolu-tion. The objective is to determine the influence of mechatronics on the different types of machines that exist today and analyze its implications.

Abstract:

The accelerated digitalization of industrial ecosystems has positioned the Internet of Things (IoT) as a critical enabler of corporate sustainability within Industry 4.0. However, evidence on how IoT contributes to environmental, social, and economic performance remains fragmented. This study conducts a systematic literature review following PRISMA 2020 guidelines to consolidate the scientific advances linking IoT with sustainable corporate management. The search covered 2009–2025 and included publications indexed in Scopus, EBSCO Essential, and MDPI, identifying 62 empirical and conceptual studies that met the inclusion criteria. Bibliometric analyses—such as keyword co-occurrence mapping and temporal heatmaps—were performed using VOSviewer to detect dominant research clusters and emerging thematic trajectories. Results reveal four domains in which IoT significantly influences sustainability: (1) resource-efficient operations enabled by real-time sensing and predictive analytics; (2) energy optimization and green digital transformation initiatives; (3) circular-economy practices supported by data-driven decision-making; and (4) the integration of IoT with Green Human Resource Management to strengthen environmentally responsible organizational cultures. Despite these advances, gaps persist related to Latin American contexts, theoretical integration, and longitudinal assessment. This study proposes a conceptual model illustrating how IoT-enabled technologies enhance corporate sustainability and offers strategic insights for aligning Industry 4.0 transformations with the Sustainable Development Goals, particularly SDGs 7, 9, and 12.

Abstract: In the study, a model was developed to calculate the power required for the circumferential cutting of solid wood in the longitudinal direction, considering the relevant technological parameters and mechanical properties of the wood. Based on measurements of different combinations and using the Response surface method (RSM) and the Central composite design (CCD), a model was created that, in its derived version, considers the cutting width and depth, the diameter and speed of the tool, the number of cutting edges and the sharpness of the cutting edge, the feed rate of the workpiece and the density and moisture content of the wood. The model can be used to calculate the cutting power of various tree species with densities ranging from 400 to 700 kg/m³, moisture contents from 8 to 16%, and a wide range of cutting-edge sharpness, from a sharp cutting edge with a tip radius of 5 µm to a blunt cutting edge with a tip radius of 35 µm. The model is designed for a rake angle of 20°, the value most frequently used in practise. An ANOVA analysis was used to determine the suitability of the model, which is highly significant with an R2 value of 0.93 and an average deviation of the calculated values from the measured values of 8.8%. The model is robust and therefore useful in the wood industry for predicting energy consumption in the processing of solid wood.

Abstract: In Al–Fe alloys, the mechanical performance is determined by the morphology of iron-rich phases. In this work, AA8176(Al-1Fe)- nY (n = 0, 0.3, 0.5, 0.7, and 0.9 wt.%) alloys were prepared by the cast method. To systematically analyze the influence of Y on microstructure evolution and tensile behavior, a multi-scale characterization approach was employed, combining metallography, electron microscopy, X-ray diffraction, cooling curve analysis, and tensile tests. The results revealed that the optimal refinement effect was achieved when the amount of Y content was 0.5 wt.%. The micro-structure of the alloy was significantly modified by Y addition. The coarse needle-like Al13Fe4 phases were gradually transformed into short rod-like and particle morphology. And the average length was decreased from 10.01 μm to 2.65 μm. Meanwhile, some small size Al10Fe2Y phases were formed around the Al13Fe4 phases. Additionally, the secondary dendrite arm spacing (SDAS) of A8176 alloy was reduced from 31.33 μm to 20.24 μm. Furthermore, the mechanical properties of the AA8176 alloy were improved due to the modified microstructure. The tensile strength of the alloy was increased from 84.47 MPa to 96.86 MPa, and the elongation was increased from 18.6 % to 23.1 %. It is proposed that the growth of α-Al dendrite and Al13Fe4 phases were effectively inhibited by segregation of Y atoms around α-Al dendrite and Al13Fe4 phases during solidification. And the Al10Fe2Y phases were formed by these Y atoms with Al and Fe elements. However, the formation of coarse Al10Fe2Y phases was promoted by excessive Y content, which resulting in a substantial degradation in mechanical properties.