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

Author:

Weiss Eyal1ORCID,Caplan Shir1,Horn Kobi1,Sharabi Moshe1

Affiliation:

1. Technology Department, Cybord.ai, Tel-Aviv 6744332, Israel

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 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.

Funder

Israel Innovation Authority

Publisher

MDPI AG

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1. An investigation of deep learning approaches for efficient assembly component identification;Beni-Suef University Journal of Basic and Applied Sciences;2024-08-19

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