Search Results (48)

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

This study aims to provide an overview of Zero Defect, Zero Waste, and non-destructive inspection technologies (NDITs), which play a crucial role in the early detection of defects and material consumption in industrial processes. Integrating Zero Defect and Zero Waste strategies with non-destructive inspection technologies supports Industry 4.0 by using advanced sensors, robotics, and AI to create smart manufacturing systems that optimise resources and improve quality. The analysis covers the main functionalities, applications and technical specifications of several NDITs to automate the inspection of industrial processes. It also discusses both the benefits and limitations of these techniques through benchmarking. Deploying inspection as a service solution based on NDITs with data-driven decision-making Artificial Intelligence for in-process or in-line inspection policies increases production control by reducing material waste and energy use, and by optimising the final factory cost. After a comprehensive assessment, this paper aims to examine and review recent developments in the Zero Defects and Zero Waste field due to emerging non-destructive inspection systems, and their combination with other technologies, such as augmented reality. Advances in sensors, robotics, and decision-making processes through Artificial Intelligence can increase Human–Robot Collaboration in the inspection process by enhancing quality assurance during production. Full article

Accurate and efficient measurement of deposited droplets’ volume is vital to achieve zero-defect manufacturing in inkjet printed organic light-emitting diode (OLED), but it remains a challenge due to droplets’ featurelessness. In our work, coherence scanning interferometry (CSI) is utilized to measure the volume. However, the CSI redundant sampling and image degradation led by the sample’s transparency decrease the efficiency and accuracy. Based on the prior degradation and strong representation for context, a novel method, volume measurement via fringe distribution module (VMFD), is proposed to directly measure the volume by single interferogram without redundant sampling. Firstly, the 3D point spread function (PSF) for CSI imaging is modeling to relate the degradation and image. Secondly, the Zernike to PSF (ZTP) module is proposed to efficiently compute the aberrations to PSF. Then, a physics aberration restoration network (PARN) is designed to remove the degradation via the channel Transformer and U-net architecture. The long term context is learned by PARN and beneficial to restoration. The restored fringes are used to measure the droplet’s volume by constrained regression network (CRN) module. Finally, the performances on public datasets and the volume measurement experiments show the promising deblurring, measurement precision and efficiency. Full article

CMOS image sensor (CIS) semiconductor products are integral to mobile phones and photographic devices, necessitating ongoing enhancements in efficiency and quality for superior photographic outcomes. The presence of white pixels serves as a crucial metric for assessing CIS product performance, primarily arising from metal impurity contamination during the wafer production process or from defects introduced by the grinding blade process. While immediately addressing metal impurity contamination during production presents challenges, refining the handling of defects attributed to grinding blade processing can notably mitigate white pixel issues in CIS products. This study zeroes in on silicon wafer manufacturers in Taiwan, analyzing white pixel defects reported by customers and leveraging machine learning to pinpoint and predict key factors leading to white pixel defects from grinding blade operations. Such pioneering practical studies are rare. The findings reveal that the classification and regression tree (CART) and random forest (RF) models deliver the most accurate predictions (95.18%) of white pixel defects caused by grinding blade operations in a default parameter setting. The analysis further elucidates critical factors like grinding load and torque, vital for the genesis of white pixel defects. The insights garnered from this study aim to arm operators with proactive measures to diminish the potential for customer complaints. Full article

Zero-Defect Manufacturing (ZDM) is a promising strategy for reducing errors in industrial processes, aligned with Industry 4.0 and digitalization, aiming to carry out processes correctly the first time. ZDM relies on digital tools, notably Artificial Intelligence (AI), to predict and prevent issues at both product and process levels. This study’s goal is to significantly reduce errors in machining large parts. It utilizes data from process models and in situ monitoring for AI-driven predictions. AI algorithms anticipate part deformation based on manufacturing data. Mechanistic models simulate milling processes, calculating tool deflection from cutting forces and assessing geometric and dimensional errors. Process monitoring provides real-time data to the models during execution. The research focuses on a high-value component from the oil and gas industry, serving as a test piece to predict geometric errors in machining based on the deviation of cutting forces using AI techniques. Specifically, an AISI 1095 steel forged flange, intentionally misaligned to introduce error, undergoes multiple milling operations, including 3-axis roughing and 5-axis finishing, with 3D scans after each stage to monitor progress and deviations. The work concludes that Support Vector Machine algorithms provide accurate results for the estimation of geometric errors from the machining forces. Full article

For semiconductor manufacturing, automatic optical inspections (AOIs) are important for chip quality inspection. An AOI system contains a robot arm, an industrial camera, a x-y platform, and a visual inspection module. Using the industrial camera, a wafer map can be obtained and then sent to the visual inspection module to compare with qualified chip features. There is a baseline in the x-y platform. Due to the limitations of the robot arm flexibility, it is difficult for the robot arm to control the angles between the chip orientation and the baseline every time, which decreases the defect recognition accuracy. This work aims to improve the defect recognition accuracy and efficiency of the AOI system. Specifically, an efficient method is presented to calculate the angle between the baseline and chip orientation. Then, the wafer map can be rotated, such that the angle equals to zero. Further, a powerful system is established to recode the rotated chip coordinate, such that the unqualified chip positions can be located efficiently. This method is called a central array method. The central array method with deep learning methods forms an AI-based AOI system. Extensive experiments demonstrate that our proposed method performs well in improving the chip quality inspection efficiency and accuracy. Nevertheless, the proposed method still has challenges in implementation since it requires integration with the manufacturing line. Full article

In recent years, paradigms like production quality or zero-defect manufacturing have emerged, highlighting the need to improve quality and reduce waste in manufacturing systems. Although quality can be analyzed from various points of view during different stages of a manufacturing system’s lifecycle, this research focuses on a multidomain simulation model definition oriented toward the analysis of productivity and geometric quality during early design stages. To avoid inconsistencies, the authors explored the definition of descriptive models using system modeling language (SysML) profiles that capture domain-specific semantics defining object constraint language (OCL) rules, facilitating the assurance of model completeness and consistency regarding this specific knowledge. This paper presents a SysML profile for the simulation of geometric deviation propagation in multistage manufacturing systems (SysML4GDPSim), containing the concepts for the analysis of two data flows: (a) coupled discrete behavior simulation characteristic of manufacturing systems defined using discrete events simulation (DEVS) formalism; and (b) geometric deviation propagation through the system based on the geometrical modeling of artifacts using concepts from the topologically and technologically related surfaces (TTRS) theory. Consistency checking for this type of multidomain simulation model and the adoption of TTRS for the mathematical analysis of geometric deviations are the main contributions of this work, oriented towards facilitating the collaboration between design and analysis experts in the manufacturing domain. Finally, a case study shows the application of the proposed profile for the simulation model of an assembling line, including the model’s transformation to Modelica and some experimental results of this type of analysis. Full article

Closed-loop manufacturing is crucial in Industry 4.0, since it provides an online detection–correction cycle to optimize the production line by using the live data provided from the product being manufactured. By integrating the inspection system and manufacturing processes, the production line achieves a new level of accuracy and savings on costs. This is far more crucial than only inspecting the finished product as an accepted or rejected part. Modeling the actual surface of the workpiece in production, including the manufacturing errors, enables the potential to process the provided live data and give feedback to production planning. Recently introduced “skin imaging” methodology can generate 2D images as a comprehensive digital twin for geometric deviations on any scanned 3D surface including analytical geometries and sculptured surfaces. Skin-Image has been addressed as a novel methodology for continuous representation of unorganized discrete 3D points, by which the geometric deviation on the surface is shown using image intensity. Skin-Image can be readily used in online surface inspection for automatic and precise 3D defect segmentation and characterization. It also facilitates search-guided sampling strategies. This paper presents the implementation of skin imaging for primary engineering surfaces. The results, supported by several industrial case studies, show high efficiency of skin imaging in providing models of the real manufactured surfaces. Full article

Industry 5.0 emphasises social sustainability and highlights the critical need for personnel upskilling and reskilling to achieve the seamless integration of human expertise and advanced technology. This paper presents a methodological framework for designing personalised training programs that support personnel upskilling, with the goal of fostering flexibility and resilience amid rapid changes in the industrial landscape. The proposed framework encompasses seven stages: (1) Integration with Existing Systems, (2) Data Collection, (3) Data Preparation, (4) Skills-Models Extraction, (5) Assessment of Skills and Qualifications, (6) Recommendations for Training Program, (7) Evaluation and Continuous Improvement. By leveraging Large Language Models (LLMs) and human-centric principles, our methodology enables the creation of tailored training programs to help organisations promote a culture of proactive learning. This work thus contributes to the sustainable development of the human workforce, facilitating access to high-quality training and fostering personnel well-being and satisfaction. Through a food-processing use case, this paper demonstrates how this methodology can help organisations identify skill gaps and upskilling opportunities and use these insights to drive personnel upskilling in Industry 5.0. Full article