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

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 an...

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Bibliographic Details
Published inElectronics (Basel) Vol. 13; no. 8; p. 1551
Main Authors Weiss, Eyal, Caplan, Shir, Horn, Kobi, Sharabi, Moshe
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2024
Subjects
Algorithms
Assembly
Assembly lines
Automation
Big Data
Circuit components
Compliance
Corrosion
Deep learning
Defects
Electronic components
Electronic components industry
Evaluation
Image processing
Machinery
Magneto-electric machines
Manufacturing
Network reliability
Quality control
Quality management
Quality standards
Real time
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Summary: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 reliability. Leveraging rapid network protocols such as gRPC and orchestration via Kubernetes, in conjunction with C++ programming and TensorFlow, this approach achieves an impressive average turnaround time of less than 5 milli-seconds. Rigorously tested on 20 operational production machines, it not only ensures adherence to IPC-A-610 and IPC-STD-J-001 standards but also optimizes production efficiency and reliability.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13081551