Abstract: Four compositions of rapidly quenched ribbon brazing alloys based on the Ag-Cu-Ti (Ag-26.5Cu-1.5Ti, Ag-25Cu-5Ti) and Ag-Cu-Zr (Ag-26.5Cu-1.5Zr, Ag-25Cu-5Zr) sys-tems were produced. Initial ingots were synthesized by arc melting. Rapidly solidified ribbons, 50-100 μm thick, were then fabricated from homogenized ingots using a "Crystall-702" facility. A comparative analysis of the microstructure and phase com-position of both the ingots and ribbons was conducted using scanning electron mi-croscopy and X-ray diffraction. The analysis revealed the presence of Cu4Ti and CuTi intermetallic compounds in the Ag-Cu-Ti alloys, and AgCu4Zr and Zr2Cu in the Ag-Cu-Zr alloys. Rapid quenching was found to produce metastable structures and significantly refine the intermetallic phases. Microhardness measurements of the ingot and ribbon states demonstrated a substantial influence of processing route on me-chanical properties. The tensile strength of the ingots was also evaluated. Wetting an-gles of the rapidly quenched alloy melts on 99% Al2O3 (alumina) ceramic substrates under vacuum were determined. All produced ribbons, except for the Ag-26.5Cu-1.5Zr composition, demonstrated adequate wettability. Thus, these materials are considered promising for further research into heat-resistant metal-ceramic joints.

Abstract: Rotating machinery is crucial element in mechanical equipment, and during serving cycle their failure is inevitable because of artificial and non-artificial reasons. Signal processing techniques are available to diagnose the failure. Due to the nonlinearity and simplicity in computation rules and the richness in theoretical system, mathematical morphology (MM) has received significant research attention in this area, and numerous papers had been published in academic journals, conference proceedings, etc. The review paper attempts to overview the morphological framework and to summarize these applications grouped as rolling element bearing and gear. Finally, the relevant discussions on MM are analyzed, and several potential prospects are suggested.

Abstract:

This paper presents the experimental validation of a computational kinematic model for a passive Stewart–Gough platform equipped with modified Cardan joints. The introduced joint geometry significantly increases structural stiffness but invalidates the standard spherical joint assumption commonly used in hexapod kinematic formulations. To address this, we develop an efficient numerical optimization based framework that solves both the direct and inverse kinematics without relying on simplified joint models. Furthermore, to enable autonomous and absolute pose measurement, each cylindrical leg joint of the platform is equipped with a LinACE™ absolute linear encoder. This sensor integration transforms the passive mechanism into an IoT-enabled reconfigurable fixture capable of internally sensing, tracking, and recalling its own configuration. The direct kinematics are computed iteratively using a homogeneous-transformation formulation and benchmarked against analytical models derived for ideal spherical joints. Experimental results demonstrate sub-millimeter accuracy in pose estimation, confirming the validity of the proposed kinematic model and highlighting the suitability of the sensor-equipped hexapod for industrial flexible fixturing applications.

Abstract: This study aims to establish a comprehensive evaluation framework for assessing project feasibility in logistics operations. The primary focus is on developing a set of criteria and assigning weights to each, ensuring a robust decision-making process. The key criteria include Safety & Asset Integrity, Net Present Value (NPV) Efficiency, Operational Supply, Operational Distribution, and Terminal & New Business Flexibility. The research utilizes the Analytic Hierarchy Process (AHP) method to obtain criteria weights. Data were collected through questionnaires filled out by experts, and the geometric mean (GM) was used to consolidate responses. The priority vector was then calculated to determine the relative importance of each criterion. To ensure the validity and consistency of the pairwise comparisons, consistency checks were performed using maximum eigenvalue (λ_max), Consistency Index (CI), and Consistency Ratio (CR). Only matrices with a CR below 0.1 were considered consistent. The results indicate that "Safety & Asset Integrity" is the most critical criterion, with a weight of 38%, followed by "NPV Efficiency" at 22%. Sub-criteria within each aspect were also weighted, revealing the importance of factors such as "Safety" (35%) and "Supply Options" (42%). Experts reviewed and approved the weighting process and evaluation metrics to ensure they aligned with real-world logistics priorities. In conclusion, the study successfully establishes a weighted rubric for logistics project evaluation, validated by expert feedback. This framework provides a structured and consistent approach for decision-makers to assess logistics projects based on multiple criteria, ensuring an informed and balanced evaluation.

Abstract: The increasing integration of collaborative robots into industrial manufacturing processes necessitates continuous improvements in their mechanical properties. While collaborative robots are designed to enable flexible and safe human-robot interactions, their comparatively low structural stiffness poses a challenge for high-precision machining and heavy assembly tasks. Addressing this limitation is essential for enhancing performance by reducing deformation-related errors and improving overall efficiency in manufacturing processes. This paper proposes an approach for enhancing the stiffness of collaborative robots by means of situational coupling. Initially, an analysis is conducted to determine the kinematic limitations of coupled collaborative robots. Subsequently, the stiffness of coupled collaborative robots is modelled using the finite element method. Experimental stiffness measurements of a collaborative robot are utilized to validate this model. On basis of this model, it is demonstrated, that the approach of coupling has the potential to enhance the stiffness by up to 37.19 times in comparison with a solitary collaborative robot.

Abstract:

The increasing demand for sustainable materials in electrical engineering has encouraged the substitution of conventional fillers in epoxy insulation with recycled industrial by-products. This study investigates the potential use of waste tire rubber particles and zinc oxide recovered from electric arc furnace dust as eco-friendly fillers for epoxy resins in high-voltage insulation applications. Four material variants were fabricated: pure epoxy, epoxy with 10 wt% ZnO, epoxy with 10 wt% tire rubber, and epoxy with 20 wt% tire rubber. The breakdown voltage of each composite was measured under AC voltage. Results indicate that the incorporation of recycled fillers influences the breakdown voltage depending on both the type and concentration of filler. The 10 wt% ZnO-filled epoxy exhibited a moderate enhancement in breakdown voltage compared to pure epoxy, attributable to interfacial polarization and charge trapping at the epoxy-ZnO interface. Conversely, tire rubber fillers introduced localized field distortion and interfacial voids, resulting in a gradual reduction of breakdown voltage with increasing filler content. The results show that ZnO from metallurgical waste can function as an effective additive to improve dielectric performance. This approach supports circular-economy principles and offers a sustainable option for future high-voltage insulation materials.

Abstract: Effective spare parts management (SPM) is imperative for equipment-intensive organizations to reduce equipment downtime through maintenance. Despite the extensive availability of data-driven SPM methodologies, decision-makers are challenged and tend to rely on tacit knowledge and simple approaches due to extensive data-gathering requirements and fragmented information across multiple organizational IT systems and departmental knowledge silos. A review of 60 academic SPM contributions demonstrated that data remains siloed and that research is limited in integrating data across SPM-relevant knowledge areas. This study proposes an empirical SPM data model to address this gap by consolidating and linking spare parts with maintenance, logistics, inventory, and equipment data, thus forming a coherent database across the identified SPM knowledge areas to bridge data silos and reduce data-gathering requirements. A case study assesses the effects of model implementation for decision-making on 10,843 spare parts and shows that model implementation led to a 15.1% stock value reduction, a 76–91% full-time equivalent resource improvement, a 4–5% decision quality improvement, and enhancement of decision-maker engagement. The data model reduces data-gathering efforts, enhances data accessibility, and improves decision quality and consistency.

Abstract: The dimensional stability of the SealCap, a critical polymer component in cosmetic dispenser pumps, is crucial for ensuring consistent discharge volume and preventing functional defects. This study aimed to optimize the injection molding process for high-precision SealCaps by analyzing changes in machine type and processing conditions. We conducted a comparative analysis between a large 64-cavity high-speed machine and a small 12-cavity precision machine. For the precision machine, three conditions were tested by varying the holding pressure time (3.0–3.5 s), resulting in cycle times of 7.2–7.7 s. The dimensional stability of the SealCap's outer diameter and the discharge volume of assembled pumps were evaluated using process capability indices (Cp and Cpk). The results demonstrated that the small precision machine under condition C (7.7 s cycle time) achieved the highest process capability, with a dimensional Cpk of 1.91, significantly outperforming the large high-speed machine (Cpk = 0.53). Similarly, the discharge volume Cpk was highest under condition C at 1.31, indicating superior functional consistency. These findings indicate that a longer cycle time enhances process stability and reduces inter-cavity variation, leading to improved dimensional quality. Therefore, condition C was identified as the optimal manufacturing process, confirming that utilizing a precision injection molding machine with optimized parameters is a highly effective strategy for producing high-quality, high-precision polymer components.

Abstract: This work investigates the application of generative design in the development of gear wheels, with emphasis on the relationship between geometry, manufacturing technology, and material selection. Material properties such as strength, stiffness, fatigue resistance, and manufacturability significantly influence both the achievable level of shape optimization and the resulting weight reduction. Generative design enables the adaptation of gear-body geometry to the mechanical characteristics of individual material classes, supporting efficient material utilization while ensuring the required stiffness. The study evaluates the effects of various manufacturing technologies—additive manufacturing, subtractive machining, and casting—by considering material processability and structural constraints. The results demonstrate that the combination of material and manufacturing method substantially shapes the design space: additive manufacturing allows lightweight and organic structures with high geometric freedom; milling requires tool-accessible geometries; and casting benefits from local reinforcement in the area around lightening holes, improving stiffness and stability. The analyses confirm that material selection is a decisive factor in the effectiveness of generative gear design, as an appropriate choice contributes to an improved balance between weight, stiffness, and production cost. The presented framework integrates shape optimization, load evaluation, and material constraints to support the development of innovative, efficient, and manufacturable gear geometries for modern engineering applications.

Abstract: Predictive maintenance (PdM) often fails to progress beyond pilot projects because machine-learning-based anomaly detection requires expert knowledge, extensive tun-ing, and labeled fault data. This paper presents an automated prototype that builds and evaluates multiple anomaly-detection models with minimal manual configura-tion. The prototype automates feature creation, model training, hyperparameter search, and ensemble construction, while allowing domain experts to control how anomaly alerts are triggered and how detected events are reviewed. Developed in a multi-year photovoltaic (PV) solar-farm case study, it targets operational anomalies such as sudden drops, underperformance periods, and abnormal drifts, using expert validation and synthetic benchmarks to shape and evaluate anomaly categories. Ex-periments on the real PV data, a synthetic PV benchmark, and a machine-temperature dataset from the Numenta Anomaly Benchmark show that no single model performs best across datasets. Instead, diverse base models and both rule-based and stacked en-sembles enable robust configurations tailored to different balances between missed faults and false alarms. Overall, the prototype offers a practical and accessible path toward PdM adoption by lowering technical barriers and providing a flexible anoma-ly-detection approach that can be retrained and transferred across industrial time-series datasets.

Abstract: Industrial heritage movable assets, particularly the traditional machining tools used in manufacturing processes, are facing an increasing risk of disappearing due to the con-tinuous advance of technology. Innovations in industry have progressively displaced many of these manual tools, making them obsolete or irrelevant in current manufacturing processes, remaining only for artisanal work. In this context, manual vertical drilling presses, which have played a crucial role in manufacturing for decades, are being dis-placed by more advanced machining tools, which incorporates, technologies such as Computer Numerical Control (CNC). This work focuses on the development of a manual vertical drill press digital model, in order to virtually recreate its operation and structure. The software used to develop the model was SolidWorks. This model aims not only to preserve a historically significant machine but also to serve as an educational resource, illustrating drilling operations before modern technologies emerged. Reconstructing them in 3D enhances the study and understanding of their mechanics and utility, ensuring access to technical knowledge and preserving their legacy in a digitalized world.

Abstract: This study introduces an end-to-end framework for assessing the condition of road markings in high-resolution drone imagery by jointly localizing, classifying the type of road marking, and quantifying damage. Candidate markings are first detected with YOLOv9, enabling robust instance discovery across complex urban scenes. For fine delineation, detections from YOLOv9 are cropped and segmented by a standalone VGG16-UNet, which refines boundaries and object structures. Condition is then estimated at the pixel level by modeling appearance statistics with kernel density estimation (KDE) and Gaussian mixture modeling (GMM) to separate intact from distressed material. From these distributions, we derive a per-instance damage ratio summarizing the proportion of degraded pixels within each marking. All outputs are georeferenced to real-world coordinates, supporting map-based visualization and integration into road asset inventories. Experiments in unseen areas demonstrate consistent generalization, with performance reported using standard detection (precision, recall, mAP) and segmentation (IoU) metrics, alongside analyses of damage ratio stability and runtime. The results show that the proposed pipeline reliably identifies road markings, estimates their damage levels, and anchors findings in geographic space, offering actionable evidence for inspection prioritization and maintenance planning. Limitations and future work include broader category coverage, improved modeling under extreme lighting conditions, and cross-city validation.

Abstract: Coal as reducing agent during the pyrometallurgical copper refinement in the anode furnace leads to high-concentration CO in the flue gas, severely hindering the resource recovery of SO2 from the flue gas. This problem may be resolved via installing a combustion chamber downstream, which introduces air to assist CO oxidation. However, the complex composition in anode furnace flue gas affects CO combustion reaction, and the flue gas temperature may decrease significantly during flowing to the combustion chamber, making CO combustion difficult. Additionally, the significant air leakage in anode furnace makes it difficult to determine the volume of flue gas, which hinders the calculation of oxygen agent amount needed in combustion chamber. In this study, an anode furnace with single production copper output of 160-ton class was selected, and its flue gas volume as well as the required air supply for complete CO combustion was calculated based on the CO concentration via adopting the elements conservation law. When CO accounts for 3-10% of total flue gas volume, the total flue gas flow rate ranges from 6800.3-7637.3 Nm³ during the reduction in the anode furnace, and the required air supply for CO burn off ranges from 545.1 m³ to 1617.9 m³. Based on the flue gas compo-sition and conditions at the combustion chamber, the influences of temperature, CO₂, and H₂O concentration on CO oxidation were systematically investigated via using a tube reactor experimental system. CO oxidation initiated at 500 °C and reached near-complete conversion (99.9%) at 800 °C. The addition of 5% H₂O notably enhanced the reaction, reducing the T50 (50% conversion temperature) from 675 °C to 650 °C. Conversely, a marked suppression was observed with 6.09% CO₂ at 650 °C, where the oxidation rate dropped sharply from 50.27% to 27.75%. A dedicated examination of O₂ then confirmed that increasing its concentration effectively enhances combustion completeness under the optimized conditions. At 650 °C, the CO oxidation rate increased from 24% to 56% as O₂ rose from 17.58% to 41%, whereas a further increase in O₂ to 51% suppressed the rate to 39%.

Abstract: Script-based computer-aided design (CAD) tools offer accessible, open, and highly customizable design environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. Here, cognitive difficulty refers to challenges in curve interpretation, spatial reasoning, and mentally mapping geometric intent into parametric scripts, while computational difficulty relates to the mathematical effort, syntax precision, and debugging complexity required to generate valid geometries. Recent studies show that users frequently struggle with this combined burden, highlighting the need for more guided and intuitive workflows. This study presents a unified, open-source framework that addresses these challenges by enabling concept-to-CAD transformation through 2D point-based representations. The framework integrates an Interactive Point Cloud Modeling (IPCM) Layer with a set of modular systems for curve construction, point generation, transformation, and data formatting, together with script-based rendering functions for generating CAD geometry. These components allow users to generate geometrically valid digital models without navigating the heavy geometric calculations, strict syntax requirements, and debugging demands typical of script-based CAD workflows. Six structured case studies demonstrate the workflow across mechanical, artistic, and handcrafted forms, while additional examples highlight its applicability to cultural motif digitization, historical alphabet reconstruction, reverse engineering, and educational prototyping. These demonstrations also show that the framework can generate fabrication-ready outputs—including volumetric models, 2D profiles, and vector representations—highlighting its adaptability across diverse design and manufacturing contexts. All systems and functions are made publicly available, enabling the entire pipeline to be carried out using free and open-source tools. By providing a practical and reproducible basis for point-based modeling, the framework advances computational design practice and supports wider adoption of script-based CAD workflows.

Abstract: This study investigates whether coopetition networks enhance manufacturing efficiency in Small and Medium-sized Enterprises (SMEs), using Portuguese Ornamental Stone firms as an empirical case. Grounded in Service-Dominant Logic and Open Innovation, the research examines how val-ue co-creation and resource integration within a technology-enabled coopetition network influ-ence key efficiency determinants. A two-phase data collection process was conducted over 108 days, comparing State-of-the-Art Practices (SoAP) with Coopetition Network Practices (CnP) supported by an IIoT-based system (Cockpit4.0+). Four manufacturing KPIs, First Time Through, Performance Efficiency, Raw Material Yield, and Labour Productivity, were assessed to evaluate operational improvements. The results show substantial gains in production output, material utili-zation, labour productivity, and throughput, while maintaining product quality. These findings confirm that coopetition fosters innovation, optimizes resource use, and enhances overall manu-facturing efficiency in SMEs. Limitations include the focus on a single sector, short-term analysis, and the absence of financial and customer-related metrics. The study provides empirical evidence of coopetition's value as a strategic approach for improving SME competitiveness. It highlights di-rections for future research on long-term impacts and the role of digital technologies in strength-ening coopetition networks.

Abstract: Quality assurance in aluminum die casting is critical, as internal defects—such as porosity—can compromise structural integrity and significantly reduce component service life. In the cost-sensitive manufacturing environment of Germany, early and automated rejection of defective parts is essential to minimize scrap, rework, and energy waste. This study investigates the feasibility and performance of deep learning for automated defect detection in industrial X-ray images of two series-production aluminum die-cast components. A systematic methodology was employed: first, candidate object-detection frameworks (YOLOv5 vs. Faster R-CNN) were evaluated under real-time constraints (< 2 s per image) on standard industrial hardware; subsequently, position-specific and single global models were trained on annotated datasets. A systematic hyperparameter study—focusing on input resolution, learning rate, and loss weights—was conducted to optimize accuracy and robustness. The best-performing models achieved F1-scores up to 0.87, with position-specific models outperforming the single global model on average. The approach was validated under real production conditions at Hengst SE (Nordwalde), demonstrating practical feasibility, strong acceptance among quality professionals, and significant potential to accelerate inspections and standardize decision-making. The results confirm that deep learning is a viable alternative to rule-based image processing and holds substantial promise for automating X-ray inspection workflows in aluminum die casting—contributing to both operational efficiency and sustainability goals.

Abstract: Low-energy impacts have been demonstrated to cause damage and failure in aircraft structures, thereby affecting the structural load-bearing performance and creating safety hazards. In this study, an innovative damage-monitoring method based on a fiber Bragg grating (FBG) is proposed for honeycomb sandwich composites. The proposed method is applicable to honeycomb sandwich composites and integrates a light gradient boosting machine (LightGBM)-optimized impact localization method with feature-parallel and data-parallel processing in the machine-learning architecture. An impact localization algorithm is applied to honeycomb sandwich composites using an array of multiplexed FBG sensors. The proposed algorithm exhibited substantial localization accuracy. The LightGBM method was employed to identify the optimal branching points for impact localization in real time, addressing the low-accuracy challenge in localizing low-energy impacts on the board structure when the fiber grating sensing system operates at a high sampling frequency.

Abstract: This work develops a design framework for hydrodynamic cavitation reactors featuring Venturi throats with a Reuleaux-triangle cross-section (VRA) and a twisted, controlled-swirl variant (VRAt). The framework links internal geometry and flow patterns to three objectives: increasing cavitation event density, improving spatial uniformity and guiding localization in target regions near the wall. Geometric indicators relate the perimeter-to-area ratio and cross-sectional area to pressure drop, nucleation-site availability and near-wall coverage, and are combined with a kinematic description of swirl-induced flow-path elongation in VRAt. At equal cross-sectional area, VRA increases the perimeter, enhances fluid–wall contact and is expected to support a more extended and homogeneous cavitation field than a circular throat. VRAt further extends the flow path, shifts the pressure minimum and intensifies near-wall localization of collapses, with potential benefits for selectivity and energy efficiency. This theoretical contribution is intended to inform the design and experimental validation of next-generation cavitating devices. The proposed criteria are expressed in measurable quantities and enable transparent comparison with circular Venturi designs, providing a basis for model development, control strategies and scale-up across applications such as water and wastewater treatment, food and beverage processing, bioenergy, biotechnology, fine chemicals, materials processing and thermal systems.

Abstract: This study constructs and refines a dynamic simulation framework that brings together risk and effectiveness factors affecting qualified workforce allocation in multi-project contexts, specifically in the construction of industrial production facilities. Based on a case study of three overlapping projects in West Java, Indonesia, the paper examines significant challenges in estimating and planning the requirements for an expert workforce across the Engineering, Procurement, and Construction (EPC) phases. In fact, mitigation measures generally assume that a qualified expert workforce is immediately available. However, getting the right personnel for a project takes time and must be done in line with the specific qualifications. To fill this gap, the paper presents a dynamics-based model that measures project risks and execution effectiveness to determine the need for the expert workforce at the multi-project level. The mixed-methods strategy includes the literature study, variable validation, simulation modeling, and case analysis. The results show that workforce planning based on risk and effectiveness significantly improves project delivery by anticipating expert workforce shortages and reducing the need for reactive solutions. The model contributes to sustainability by enhancing long-term workforce resilience, reducing resource waste, and supporting more efficient industrial project delivery.

Abstract: This study examines significant research deficiencies in the procurement management difficulties encountered by Chinese contractors in international EPC projects within the Belt and Road Initiative (BRI). This research addresses: 1) the distinct procurement inefficiencies (e.g., communication delays, material shortages) faced by Chinese contractors in global projects; 2) the impact of supply chain integration on improving procurement performance in international EPC projects; 3) the application of social network analysis (SNA) to investigate inter-organizational relationships in procurement management; and 4) the influence of stakeholder collaboration on optimizing procurement processes. The results indicate that efficient supply chain integration markedly enhances procurement efficiency, minimizes delays, and decreases costs. The study indicates that robust collaboration and communication across stakeholders, namely contractors, suppliers, subcontractors, and designers—are essential for lowering procurement risks and facilitating seamless project execution. Social network analysis underscores the significance of principal stakeholders and their interrelations in enhancing procurement results, asserting that effectively coordinated networks facilitate superior resource allocation and risk management. Moreover, case studies indicate that Chinese contractors utilize tactics such just-in-time inventory management, supplier relationship management, and digital procurement platforms to mitigate supply chain risks and improve procurement performance. The study highlights that supply chain integration, along with robust stakeholder collaboration, reduces inefficiencies and offers a competitive edge for Chinese contractors in the global EPC industry.