Search Results (56)

This study develops an integrated quality inspection and production optimization framework for an imperfect production system, where system deterioration follows a zero-inflated non-homogeneous Poisson process (ZI-NHPP) characterized by a power-law intensity function. Parameters are estimated from historical data using the Expectation-Maximization (EM) algorithm, with a zero-inflation parameter π modeling scenario where the system remains defect-free. Operating in either an in-control or out-of-control state, the system produces products with Weibull hazard rates, exhibiting higher failure rates in the out-of-control state. The proposed model integrates system status, defect rates, employee efficiency, and market demand to jointly optimize the number of conforming items inspected and the production run length, thereby minimizing total costs—including production, inspection, correction, inventory, and warranty expenses. Numerical analyses, supported by sensitivity studies, validate the effectiveness of this integrated approach in achieving cost-efficient quality control. This framework enhances quality assurance and production management, offering practical insights for manufacturing across diverse industries. Full article

High volume pencil manufacturing often generates substantial material waste due to a small proportion of products having missing or recessed graphite leads. Standard vision-based quality control processes discard entire wooden slats that carry any faulty pencils, causing excessive waste of usable wood and graphite resources. This study describes the design and implementation of a laser triangulation-based inspection system for lead defect detection after individual pencils are cut from the slat. The system combines a two-dimensional laser profile scanner with synchronized triggering sensors and a programmable logic controller (PLC)-controlled pneumatic rejection unit. Using the systematic design methodology for VDI 2221, a functional prototype was developed, which was then tested in a simulated production system with a throughput of up to 200 pencils per minute. The proposed system was able to detect missing and recessed leads highly accurately and correctly classified 98–100% of pencils without false rejections of acceptable products. The most common type of defect was missing or deeply recessed lead with an accuracy of 98.5%, and the less common partial-lead fractures had a lower percentage of detection of nearly 92% due to geometric sensitivity. The developed inline inspection system was successful in identifying and rejecting defective pencils without the waste of materials and provided a viable alternative of economical implementation with less than a one-year payback period. Through its increased resource efficiency and decreased raw material waste, the proposed system contributes to the United Nations Sustainable Development Goals, namely SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Full article

This paper presents an integrated three-dimensional (3D) quality inspection system for mold manufacturing that addresses critical industrial constraints, including zero-shot generalization without retraining, complete decision traceability for regulatory compliance, and robustness under severe data shortages (<2% defect rate). Dual optical sensors (Photoneo MotionCam 3D and SICK Ruler) are integrated via affine transformation-based registration, followed by computer-aided design (CAD)-based classification using geometric feature matching to CAD specifications. Unsupervised defect detection combines density-based spatial clustering of applications with noise (DBSCAN) clustering, curvature analysis, and alpha shape boundary estimation to identify surface anomalies without labeled training data. Industrial validation on 38 product classes (3000 samples) yielded 99.00% classification accuracy and 99.12% macroscopic precision, outperforming Point-MAE (93.24%) trained under the same limited-data conditions. The CAD-based architecture enables immediate deployment via CAD reference registration, eliminating the five-day retraining cycle required for deep learning, essential for agile manufacturing. Processing time stability (0.47 s compared to 43.68 s for Point-MAE) ensures predictable production throughput. Defect detection achieved 98.00% accuracy on a synthetic validation dataset (scratches: 97.25% F1; dents: 98.15% F1). Full article

In this paper, we present enabling concepts for Zero Defect Manufacturing (ZDM) based on flexible human–robot interaction. We introduce the Human–Robot Multistation System (HRMS) as a novel framework for flexible, human-initiated task allocation across multiple workstations. A HRMS is defined as one or more workstations that support human–robot collaborative task execution and integrate intelligent perception and interaction systems with coordination logic, enabling alternating or collaborative task execution. These components allow human workers to interact with the system through a gesture-based modality and to receive task assignments. An agent-based task scheduler responds to human-initiated ‘Ready’ signals to pace activities ergonomically. We built a laboratory demonstrator for an Industry 5.0 ZDM final inspection/rework use case and conducted a first pilot study (n = 5, internal participants) to evaluate system usability (SUS), perception (Godspeed), mental workload (NASA-TLX), completion times, and error rates. Results indicated technical feasibility under laboratory conditions and acceptable usability, with SUS 70.5 ± 22 (‘OK’ toward ‘Good’), overall GQS 3.2 ± 0.8, RAW NASA-TLX 37 ± 16.3, mean job throughput time 232.5 ± 46.5 s, and errors in 9/10 jobs (E1–E4). In simulation, a proximity-aware shortest-path heuristic reduced walking distance by up to 70% versus FIFO without productivity loss. We conclude that HRMS is feasible with acceptable user experience under lab constraints, while recurrent task-level failures require mitigation and larger-scale validation. Full article

The increasing market demand for high-power and high-frequency applications necessitates the development of highly reliable Gallium Nitride (GaN) High-Electron-Mobility Transistors (HEMTs). While GaN offers superior performance and efficiency over traditional silicon, gate-related defects pose a significant reliability challenge, often leading to premature device failure under stress. Traditional High-Temperature Gate Bias (HTGB) testing is effective but time-consuming and costly, particularly when defects are only identified post-packaging. This study focuses on developing an effective wafer-level screening methodology to mitigate the financial burden and reputational risk associated with late-stage defect discovery. Failure analysis of an HTGB premature failure revealed a gate metal deposition defect characterized by identical elemental composition to the bulk metal, suggesting a small-volume structural anomaly. Crucially, a comparative analysis showed that Forward Gate Current (I_GON) is an insensitive screening metric due to high inherent gate leakage through the passivation layer. In contrast, the Reverse Gate Current (I_GOFF) exhibited sensitivity, particularly under the tensile stress induced by package molding, which is attributed to the piezoelectric effect altering the depletion region width beneath the p-GaN gate. Based on this observation, a multi-pulse I_DSS test was developed as a wafer-level screen. This method successfully amplified the subtle electrical field perturbations caused by the gate defect. After screening 231 dies using the new methodology, zero failures were recorded after 1000 h of HTGB stress, a significant improvement over the initial failure rate of 0.43% (1 out of 231). This work demonstrates that early, sensitive wafer-level screening of gate defects is indispensable for optimizing manufacturing yield and enhancing long-term device reliability. Full article

This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm. Full article

Using the method of laser ablation in liquid, submicron-sized particles of zero-valent amorphous selenium (Se SMPs) were created. A number of composite polymer materials were manufactured based on polyurea and Se SMPs at concentrations ranging 0.1–2.5 wt.%. The manufactured materials showed no significant surface or internal defects at either the macro or micro level. It was found that the Se SMPs were not uniformly distributed inside the polymer, but formed ordered areas with slightly higher and lower concentrations of the particles. It was demonstrated that the manufactured materials did not generate a significant amount of active oxygen species, which could damage biological objects such as protein molecules and DNA, while also exhibiting pronounced bacteriostatic properties without significantly affecting the growth and reproduction of mammalian cells. Materials containing 0.25 and 1% Se SMPs, when added to soil, improved the morphometric parameters of radish plants (Raphanus sativus var. sativus). These polymer composite materials based on polyurea with the addition of Se SMPs are promising functional materials for agriculture due to their antibacterial activity. Full article

Mass customisation in the automotive industry has exploded the number of wiring harness variants that must be assembled, tested and repaired on the shop floor. Existing CAD or schematic formats are too heavy and too coarse-grained to drive in-line, per-VIN validation, while supplier documentation is heterogeneous and often incomplete. This paper presents a pin-centric, two-tier graph model that converts raw harness tables into a machine-readable, wiring-aware digital twin suitable for real-time use in manufacturing plants. All physical and logical artefacts—pins, wires, connections, paths and circuits—are represented as nodes, and a dual-store persistence strategy separates attribute-rich JSON documents from a lightweight NetworkX property graph. The architecture supports dozens of vehicle models and engineering releases without duplicating data, and a decentralised validation pipeline enforces both object-level and contextual rules, reducing initial domain violations from eight to zero and eliminating fifty-two circuit errors in three iterations. The resulting platform graph is generated in 0.7 s and delivers 100% path-finding accuracy. Deployed at Ford’s Almussafes plant, the model already underpins launch-phase workload mitigation, interactive visualisation and early design error detection. Although currently implemented in Python 3.11 and lacking quantified production KPIs, the approach establishes a vendor-agnostic data standard and lays the groundwork for self-aware manufacturing: future work will embed real-time validators on the line, stream defect events back into the graph and couple the wiring layer with IoT frameworks for autonomous repair and optimisation. Full article

Carbon-fiber-reinforced polymer (CFRP) preforms are vital for high-performance composite structures, yet the real-time detection of surface yarn alignment defects is hindered by complex textures. This study introduces a novel machine vision framework to enable the precise, real-time identification of such defects in CFRP preforms. We proposed obtaining the frequency spectrum by removing the zero-frequency component from the projection curve of images of carbon fiber fabric, aiding in the identification of the cycle number for warp and weft yarns. A texture structure recognition method based on the artistic conception drawing (ACD) revert is applied to distinguishing the complex and diverse surface texture of the woven carbon fabric prepreg from potential surface defects. Based on the linear discriminant analysis for defect area threshold extraction, a defect boundary tracking algorithm rule was developed to achieve defect localization. Using over 1500 images captured from actual production lines to validate and compare the performance, the proposed method significantly outperforms the other inspection approaches, achieving a 97.02% recognition rate with a 0.38 s per image processing time. This research contributes new scientific insights into the correlation between yarn alignment anomalies and a machine-vision-based texture analysis in CFRP preforms, potentially advancing our fundamental understanding of the defect mechanisms in composite materials and enabling data-driven quality control in advanced manufacturing. Full article

Additive manufacturing (AM) is one of the most frequently used technologies to produce complex configuration products. Moreover, AM is very well known as a technology which is characterized by a low amount of generated waste and the potential to be called zero-waste technology. As is known, there are seven main groups of technologies described in the ISO/ASTM 52900 standard that allow the use of very different materials from polymers to metals, ceramics, and composites. However, the increased utilization of additively manufactured composites for different applications requires a deeper analysis of production processes and materials’ characteristics. Various AM technologies can be used to produce complex composite structures reinforced with short fibers; however, only material extrusion (MEX)-based technology is used for the production of composites reinforced with continuous fibers (CFs). At this time, five different methods exist to produce CF-reinforced composite structures. This study focuses on co-extrusion with the towpreg method. Because of the complexity and layer-by-layer nature of the process, defects can occur during production, such as poor interlayer adhesion, increased porosity, insufficient impregnation, and others. To eliminate or minimize defects’ influence on mechanical properties and structural integrity of additively manufactured structures, a hypothesis was proposed involving heat treatment. Carbon fiber’s conductive properties can be used to heal the composite structures, by heating them up through the application of electric current. In this research article, an experimental evaluation of conditions for additively manufactured composites with continuous carbon fiber reinforcement for self-healing processes is presented. Mechanical testing was conducted to check the influence of heat treatment on the flexural properties of the composite samples. Full article

Zero-defect manufacturing is one of the most promising strategies to mitigate failures within manufacturing processes, allowing industries to increase product quality efficiently and effectively. One of the challenges faced in the practical adoption of zero-defect manufacturing is that the most important aspect of manufacturing, people, is often neglected. Aiming to support shop floor operators, this work introduces a human-centric approach assisting them to become aware of defects in the production line and imminently reconfigure it. Our system comprises an Augmented Reality application that encompasses interfaces that dynamically adapt to different contexts of use and enable operators to interact naturally and effectively and reconfigure the manufacturing process. The system leverages the efficiency of the shop floor operators in monitoring and controling the production line they are working on, according to the task they are performing, and their level of expertise, to produce appropriate visual components. To demonstrate the versatility and generality of the proposed system we evaluated it in three different production lines, conducting cognitive walkthroughs with experts and user-based evaluations with thirty shop floor operators. The results demonstrate that the system is intuitive and user-friendly, facilitating operator engagement and situational awareness, enhancing operator attentiveness, and achieving improved operational outcomes. Full article

Additive manufacturing (AM) has revolutionized production across industries, yet persistent challenges in defect detection and process reliability necessitate advanced in situ monitoring solutions. While non-destructive evaluation (NDE) techniques such as X-ray computed tomography, thermography, and ultrasonic testing have been widely adopted, the critical role of 3D surface topographic monitoring remains underutilized for real-time anomaly detection. This work comprehensively reviews the 3D surface monitoring of AM processes, such as Laser powder bed fusion, directed energy deposition, material extrusion, and material jetting, highlighting the current state and challenges. Furthermore, the article discusses the state-of-the-art advancements in closed-loop feedback control systems, sensor fusion, and machine learning algorithms to integrate 3D surface data with various process signatures to dynamically adjust laser parameters and scan strategies. Guidance has been provided on the best 3D monitoring technique for each of the AM processes. Motivated by manufacturing labor shortages, the high skill required to operate and troubleshoot some of these additive manufacturing techniques, and zero-defect manufacturing goals, this paper also explores the metamorphosis towards autonomous AM systems and adaptive process optimization and explores the role and importance of real-time 3D monitoring in that transition. Full article

To address the challenges of poor image quality and low detection accuracy in low-light environments during coating workshop inspections, this paper proposes a low-light image enhancement method based on zero-reference depth curve estimation, termed Zero-PTDCE. A low-light image dataset, PT-LLIE, tailored for coating workshop scenarios is constructed, encompassing various industrial inspection conditions under different lighting environments to enhance model adaptability. Furthermore, an enhancement network integrating a lightweight denoising module and depthwise separable dilated convolution is designed to reduce noise interference, expand the receptive field, and improve image detail restoration. The network training process employs a multi-constraint strategy by incorporating perceptual loss (L_p), color loss (L_c), spatial consistency loss (L_s), exposure loss (L_e), and total variation smoothness loss (L_tv) to ensure balanced brightness, natural color reproduction, and structural integrity in the enhanced images. Experimental results demonstrate that, compared to existing low-light image enhancement methods, the proposed approach achieves superior performance in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE), while maintaining high computational efficiency. Beyond general visual enhancement, Zero-PTDCE significantly improves the visibility of fine surface features and defect patterns under low-light conditions, which is crucial for the accurate assessment of coating quality, including defect identification such as uneven thickness, delamination, and surface abrasion. This work provides a reliable image enhancement solution for intelligent inspection systems and supports both the automated operation and material quality evaluation in modern coating workshops, contributing to the broader goals of intelligent manufacturing and material characterization. Full article

The FLASH-COMP project aims to introduce novel inspection and monitoring technologies to develop a digital solution to predict defects during manufacturing, aiming to reach a zero-waste approach in composites manufacturing. Particularly, it’s studied the integration of two different Fiber Optic Sensor (FOS) technologies: Fiber Bragg Grating (FBG) and distributed All Grating Fiber (AGF^®), to retrieve relevant data during the preforming stage and later resin infusion process for aero-space materials. During the study, both FOS technologies were introduced into the materials, varying process conditions and the introduction of some artificial defects to evaluate the sensors response to correlate them after with their signals. Both systems can retrieve relevant information during the process such as vacuum, leaks and temperature changes, presence of voids and air bubbles, detection of dry zones, and resin flow monitoring. Further developments have to be focused on the scalability in the implementation, since FOS are fragile to handle and need specific training to use it in a more industrial field. Full article

Artificial intelligence (AI) has become a revolutionary tool in the automotive sector, specifically in quality management and issue identification. This article presents a systematic review of AI implementations whose target is to enhance production processes within Industry 4.0 and 5.0. The main methods analyzed are deep learning, artificial neural networks, and principal component analysis, which improve defect detection, process automation, and predictive maintenance. The manuscript emphasizes AI’s role in live auto part tracking, decreasing dependance on manual inspections, and boosting zero-defect manufacturing strategies. The findings indicate that AI quality control tools, like convolutional neural networks for computer vision inspections, considerably strengthen fault identification precision while reducing material scrap. Furthermore, AI allows proactive maintenance by predicting machine defects before they happen. The study points out the importance of incorporating AI solutions in actual manufacturing methods to ensure consistent adaptation to Industry 5.0 requirements. Future investigations should prioritize transparent AI approaches, cyber-physical system consolidation, and AI material enhancement for sustainable production. In general terms, AI is changing quality assurance in the automotive industry, improving efficiency, consistency, and long-term results. Full article