Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning

Weiss, Eyal; Caplan, Shir; Horn, Kobi; Sharabi, Moshe

doi:10.3390/electronics13081551

Electronics

2024

DOI: 10.3390/electronics13081551

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Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning

Eyal Weiss,

Shir Caplan,

Kobi Horn

et al.

Abstract: This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This proactive approach ensures that defective components are rejected before mounting, effectively preventing issues from ever occurring, thus significantly enhancing efficiency and re… Show more

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Cited by 2 publications

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An investigation of deep learning approaches for efficient assembly component identification

Ramesh,

Mushtaq,

Deshmukh

et al. 2024

Beni-Suef Univ J Basic Appl Sci

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Background Within the manufacturing sector, assembly processes relying on mechanical fasteners such as nuts, washers, and bolts hold critical importance. Presently, these fasteners undergo manual inspection or are identified by human operators, a practice susceptible to errors that can adversely affect product efficiency and safety. Given considerations such as time constraints, escalating facility and labor expenses, and the imperative of seamless integration, the integration of machine vision into assembly operations has become imperative. Results This study endeavors to construct a robust system grounded in deep learning algorithms to autonomously identify commonly used fasteners and delineate their attributes (e.g., thread type, head type) with acceptable precision. A dataset comprising 6084 images featuring 150 distinct fasteners across various classes was assembled. The dataset was partitioned into training, validation, and testing sets at a ratio of 7.5:2:0.5, respectively. Two prominent object detection algorithms, Mask-RCNN (regional-based convolutional neural network) and You Look Only Once-v5 (YOLO v5), were evaluated for efficiency and accuracy in fastener identification. The findings revealed that YOLO v5 surpassed Mask-RCNN in processing speed and attained an mean average precision (MAP) of 99%. Additionally, YOLO v5 showcased superior performance conducive to real-time deployment. Conclusions The development of a resilient system employing deep learning algorithms for fastener identification within assembly processes signifies a significant stride in manufacturing technology. This study underscores the efficacy of YOLO v5 in achieving exceptional accuracy and efficiency, thereby augmenting the automation and dependability of assembly operations in manufacturing environments. Such advancements hold promise for streamlining production processes, mitigating errors, and enhancing overall productivity in the manufacturing sector.

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An investigation of deep learning approaches for efficient assembly component identification

Ramesh,

Mushtaq,

Deshmukh

et al. 2024

Beni-Suef Univ J Basic Appl Sci

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Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques

Weiss

2024

Electronics

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This study presents an advanced methodology for improving electronic assembly quality through real-time, inline inspection utilizing state-of-the-art artificial intelligence (AI) and deep learning technologies. The primary goal is to ensure compliance with stringent manufacturing standards, notably IPC-A-610 and IPC-J-STD-001. Employing the existing infrastructure of pick-and-place machines, this system captures high-resolution images of electronic components during the assembly process. These images are analyzed instantly by AI algorithms capable of detecting a variety of defects, including damage, corrosion, counterfeit, and structural irregularities in components and their leads. This proactive approach shifts from conventional reactive quality assurance methods by integrating real-time defect detection and strict adherence to industry standards into the assembly process. With an accuracy rate exceeding 99.5% and processing speeds of about 5 ms per component, this system enables manufacturers to identify and address defects promptly, thereby significantly enhancing manufacturing quality and reliability. The implementation leverages big data analytics, analyzing over a billion components to refine detection algorithms and ensure robust performance. By pre-empting and resolving defects before they escalate, the methodology minimizes production disruptions and fosters a more efficient workflow, ultimately resulting in considerable cost reductions. This paper showcases multiple case studies of component defects, highlighting the diverse types of defects identified through AI and deep learning. These examples, combined with detailed performance metrics, provide insights into optimizing electronic component assembly processes, contributing to elevated production efficiency and quality.

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Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning

Cited by 2 publications

References 32 publications

An investigation of deep learning approaches for efficient assembly component identification

An investigation of deep learning approaches for efficient assembly component identification

Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques

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