인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study proposes an edge computing-based artificial intelligence (AI) vision inspection system for manufacturing sites and validates it through a soldering inspection case study. While AI-based vision inspection offers higher accuracy compared with conventional rule-based methods, it suffers from increased computation time, and may fail to meet the required takt times. Furthermore, relying on external cloud servers introduces latency issues and data security risks. Although on-premises servers can mitigate latency and security concerns, a single server failure can trigger severe disruptions by shutting down the entire production line. Therefore, adopting an edge computing approach is desirable to simultaneously address latency, data security, and operational reliability. In this study, instead of utilizing a dedicated inference server, both the client and server are deployed onto a single local personal computer (PC) to implement a fast, cost-effective edge computing-based AI vision inspection system. The proposed system is then applied to and verified in a soldering inspection process. Additionally, to compensate for the degraded small-object detection performance inherent in AI models, a hybrid inspection framework integrating a rule-based algorithm is applied. Experimental results demonstrate an average computation time of 150 ms that successfully satisfied the 200 ms target tact time, and the hybrid inspection accuracy for solder ball defects substantially improved from 75% to 96%.
본문·목차
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