Parameter Identification and Refinement for Parallel PCB Inspection in Cyber-Physical-Social Systems
Yansong Cao1; Yutong Wang2; Jiangong Wang3; Yonglin Tian2; Xiao Wang4; Fei-Yue Wang1
Source PublicationIEEE Transactions on Computational Social Systems
ISSN2329-924X
2023-12
Pages1-10
Corresponding AuthorWang, Xiao(xiao.wang@ahu.edu.cn)
Abstract

Replacing manual inspection, automated optical inspection (AOI) equipment is widely used in printed circuit board (PCB) factories for automatic PCB defect segmentation. However, parameter refinement of AOI devices has gradually become an efficiency bottleneck in AOI usage, posing a highly challenging task. Since a large number of AOI parameters and different types of inspected objects make timely proper parameter refinement for clear images quite difficult. Considering this, we propose the concept of parallel PCB inspection in cyber–physical–social systems (CPSSs). Based on artificial systems, computational experiments, and parallel execution (ACP) theory with automatic parameter identification and refinement, we perform descriptive intelligence to build an artificial imaging system, obtain knowledge about the mapping relationships of parameter settings and imaging results, and realize automatic parameter identification given image input; conduct predictive intelligence to obtain image quality assessment results and maximize quality score for refinement strategies; and carry out prescriptive intelligence to guide parameter refinement for better imaging. This system could guide engineers proactively with constructive suggestions on parameter refinement when imaging failures occur, greatly reducing the training cost of engineers while improving work efficiency and work quality. To validate that our parallel PCB inspection could perform automatic AOI results evaluation without human participation, we evaluate it on distortion-free and different distortion images and confirm image quality score is positively associated with segmentation accuracy.

KeywordImaging Inspection Image segmentation Image quality Hardware Training Software Automated optical inspection (AOI) parallel printed circuit board (PCB) inspection parameter identification parameter refinement
DOI10.1109/TCSS.2023.3330762
Indexed BySCI
Language英语
Funding ProjectOptima Collaborative Research Project of Defect Detection Algorithm for Automated Optical Inspection-Phase II
Funding OrganizationOptima Collaborative Research Project of Defect Detection Algorithm for Automated Optical Inspection-Phase II
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:001170469200001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory实体人工智能系统感认知
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57290
Collection多模态人工智能系统全国重点实验室_平行智能技术与系统团队
Corresponding AuthorJiangong Wang
Affiliation1.the Faculty of Innovation Engineering, Macau University of Science and Technology
2.the Institute of Automation, Chinese Academy of Sciences
3.the China Institute of Aviation Systems Engineering
4.the School of Artificial Intelligence, Anhui University
Recommended Citation
GB/T 7714
Yansong Cao,Yutong Wang,Jiangong Wang,et al. Parameter Identification and Refinement for Parallel PCB Inspection in Cyber-Physical-Social Systems[J]. IEEE Transactions on Computational Social Systems,2023:1-10.
APA Yansong Cao,Yutong Wang,Jiangong Wang,Yonglin Tian,Xiao Wang,&Fei-Yue Wang.(2023).Parameter Identification and Refinement for Parallel PCB Inspection in Cyber-Physical-Social Systems.IEEE Transactions on Computational Social Systems,1-10.
MLA Yansong Cao,et al."Parameter Identification and Refinement for Parallel PCB Inspection in Cyber-Physical-Social Systems".IEEE Transactions on Computational Social Systems (2023):1-10.
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