2019· International Journal of Intelligent Automation & Robotics Engineering· Vol 2, pp. 01-14· 0 citations
Abstract
Machine vision has become a key technology in modern industrial automation, enabling fully automated quality inspection in robotic manufacturing systems. Unlike traditional human inspection methods, machine vision offers higher accuracy, consistency, speed, and lower operational costs. These systems use cameras, lighting, lenses, image processing, pattern recognition, and AI techniques to detect defects, verify dimensions, classify products, and monitor manufacturing processes in real time. This survey reviews machine vision-based quality inspection methods in robotic manufacturing up to 2019, covering major applications in industries such as automotive, electronics, aerospace, pharmaceuticals, and food processing. It highlights advancements in feature extraction, defect detection algorithms, classification models, and robotic integration frameworks. The study shows that machine vision significantly improves inspection accuracy, reduces cycle time, and enhances manufacturing consistency, while also discussing challenges such as illumination changes, computational complexity, and system adaptability. Overall, machine vision plays a vital role in smart manufacturing and Industry 4.0 production systems.
The recent progress in imaging sensors, artificial intelligence, real-time processing hardware has led to a change in industrial quality inspection, where machine vision systems have become a key idea. The conventional manual inspection processes can be quite tedious, irregular and inapplicable to high production throughput. Machine vision systems offer fully automated, objective and repeatable inspection systems with benefits that improve the quality of the products, minimizes the cost of operations and acquires the industry with high standards. The paper is a thorough examination of machine vision systems used in industrial quality inspection especially in the areas of system architecture, image acquisition, preprocessing, feature extraction, defect detection and decision making processes. The incorporation of classical computer vision methodology with the latest deep learning systems like convolutional neural networks has achieved a high rate of quality defect detection, high resilience, and scalability in various manufacturing industries such as the automobile, electronics, pharmaceutical, and food processing industries. Moreover, the paper discusses the issues of inconsistency in lighting, real-time, imbalance in the data set, and system integration with industrial automation systems. Recent studies experimental tests have indicated that machine vision based inspection systems have the capability of having accuracy above 98% which is far much higher when compared to traditional rule based method. At the end of the paper, some future trends have been mentioned like edge-based vision system, explainable artificial intelligence, and Industry 4.0 integration which will likely characterize the future of intelligent inspection systems.
M. Bianchi· International Journal of Mod...· 0 citations
Computer vision (CV) systems driven by artificial intelligence (AI) are increasingly replacing manual and conventional rule-based inspection procedures in industrial quality inspection, enabling automated, real-time, and data-driven decision-making based on visual data. Conventional inspection procedures are usually limited in terms of scalability, human-related errors, and high operational costs, which drives the increasing reliance on smart vision-based technologies. A practical, practice-oriented review of AI-based computer vision systems for industrial quality control is provided in this paper, with emphasis on real-world deployment issues and performance aspects. Two representative industrial case studies are examined. The first investigates real-time extrusion monitoring in robotic building construction, where geometric deviations, bead-width variation, surface irregularities, and process inconsistencies are detected during material deposition using vision-based monitoring and image-processing pipelines. The second case study focuses on automated inspection of bolts and screws in manufacturing lines, addressing presence detection, orientation recognition, and defect classification under high-speed production conditions. In both cases, widely adopted vision and AI techniques, including image-processing pipelines, convolutional neural networks, and edge-computing hardware, are discussed and compared. The analysis shows that AI-enabled computer vision systems can outperform traditional rule-based or manual solutions in terms of inspection accuracy, consistency, and throughput when they are supported by reliable acquisition, representative data, and robust industrial integration. Nevertheless, challenges related to dataset quality, model generalization, lighting variability, and real-time computational constraints remain critical in industrial environments. In conclusion, AI-based computer vision plays a central enabling role in intelligent quality inspection within the context of Industry 5.0. Future research should focus on adaptive model capabilities, tighter integration with cyber-physical systems, and scalable deployment strategies to achieve reliable and autonomous inspection across diverse industrial sectors.
Maaz A. Khan, C. Vasques, A. Cavadas· Encyclopedia· 0 citations
The proposed framework integrates robotic navigation, visual sensing, image processing, defect classification, and maintenance decision support to achieve reliable inspection across diverse industrial sectors and supports predictive maintenance, improves quality assurance, and advances smart manufacturing in Industry 4.0.
Arvind Kumar Singh, Lakshmi Narayanan· International Journal of Int...· 0 citations
With automated manufacturing advancing toward high precision and high efficiency, the integration of artificial intelligence, machine vision and industrial robots has become the core pillar of intelligent manufacturing. Traditional manual inspection and scheduled maintenance suffer from high missing detection rates, delayed response and excessive operating costs, failing to satisfy strict quality control standards of modern manufacturing. Deep learning empowers machine vision to extract defect features and realize automatic classification efficiently, while industrial robots break free from fixed programming to achieve autonomous perception and decision-making. Supported by multi-sensor data fusion and machine learning, predictive maintenance enables early equipment fault identification and drastically cuts unplanned downtime. Vision-guided robotic inspection systems outperform human operators in both detection speed and precision. This paper systematically sorts out technical routes, system architectures and engineering practices of the two major application scenarios, and discusses cutting-edge directions for collaborative integration of vision and robotic technologies, providing systematic references for engineering implementation and relevant research layout.
Recommendations are formulated for choosing an optimal machine vision architecture for different types of production, and promising directions for further development of the technology in the context of industrial digital transformation are identified.
Ya.A. Gaidukov, M. Naumov· Glavnyj mekhanik (Chief Mech...· 0 citations
Automated mechanical inspection is an important requirement in modern manufacturing industries for detecting dimensional errors, surface defects, cracks, missing components, and improper assembly. Conventional inspection methods often depend on manual operators or PC-based image-processing systems, which can introduce inspection delays and inconsistent results. This work proposes an FPGA-based machine vision system for automated mechanical inspection, combining image acquisition, preprocessing, feature extraction, defect detection, and decision-making on a reconfigurable FPGA platform. A camera captures images of mechanical components placed in a controlled inspection area. The acquired image is converted into a suitable digital format and processed using FPGA-based image-processing algorithms such as grayscale conversion, noise filtering, thresholding, edge detection, and morphological operations. Extracted features are compared with predefined dimensional and quality criteria to identify defective components. The FPGA provides parallel processing and low-latency operation, making the proposed system suitable for real-time industrial inspection. The system can generate a pass/fail decision and activate a sorting mechanism through suitable control interfaces. The proposed architecture offers improved processing speed, reduced dependence on external computers, and flexible hardware implementation. It can be applied to inspection of gears, shafts, bearings, castings, welds, electronic-mechanical assemblies, and other manufactured components.
Jeyasudha S, Radhakrishnan B, Sowmiya C et al.· International Research Journ...· 0 citations
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