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All Journal IAES International Journal of Robotics and Automation (IJRA)
Chaithanya Sarangaraj
Meenakshi Sundararajan Engineering College
Automotive Engineering Electrical & Electronics Engineering

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Visual and electrical approaches for automated verification of electronic components Sowmya Santhanam; Aadhitya Swaminathan Velmurugan; Durkadevi Chandrahasan; Krithika Arcot Rathna Kumar; Jeevanthika Chepauk; Deepika SasiKumar; Chaithanya Sarangaraj
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp658-668

Abstract

Reliable inspection and sorting of electronic components have become pivotal along the path of electronic manufacturing toward higher density and automation. Automated optical inspection systems at present depend on visual assessment methods because they do not include electrical testing capabilities, which results in a component verification reliability gap. In spite of recent advances, largely due to the fact that most automated optical inspection systems still abide by visual evaluation, verification of the actual electrical behavior of components became quite impossible. This paper is focused on bridging this gap through the introduction of a unified inspection framework whereby visual analysis is executed along with programmable electrical validation under a single automated process in conformity with Industry 4.0 practices. The system's synchronized workflow includes vision-based detection, optical character recognition, resistor color-band parsing, surface defect analysis, and electrical testing in real time. The component localization task uses YOLOv5, while EasyOCR with a convolutional neural network-long short-term memory (CNN-LSTM) structure and HSV-based segmentation delivers exact value extraction results. The testing system achieved 98.4% classification accuracy, 98.9% value recognition accuracy, and 94.9% overall sorting accuracy when tested on 3,000 photos and 400 physically inspected components at a throughput rate of seven components per minute.
Co-Authors Aadhitya Swaminathan Velmurugan Deepika SasiKumar Durkadevi Chandrahasan Jeevanthika Chepauk Krithika Arcot Rathna Kumar Sowmya Santhanam