Deep SVDD 알고리즘 기반의 품질 검사 시스템 설계
Designing a quality inspection system using Deep SVDD 1)
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초록
다품종 소량 생산을 중심으로 하는 제조 기업의 경우 하나의 공정 라인에서 여러 가지의 제품들을 생산하기 때문에 자동화된 검수 보다는 작업자에 의해서 불량품을 선별하고 있다. 따라서일정한 기준 없이 작업자의 경험이나 숙련도에 의해 선별 기준이 조금씩 차이가 있어 잘못 선별이 이루어질 가능성이 높다. 또한, 크기나 모양 등이 정형화되지 않은 유연물체의 경우 선별 기준에 대한 편차가 더 커질 수 있는 문제가 있다. 이러한 문제를 해결하기 위해 본 논문에서는 인공지능 기반의 비지도 학습 방법을 적용한 품질 검사 시스템을 설계하고 실제 제조현장에서 획득한데이터 셋을 기반으로 정확도를 실험하는 연구를 진행하였다.
Abstract
In manufacturing companies that focus on small-scale production of multiple product varieties, defective products are manually selected by workers rather than relying on automated inspection. Consequently, there is a higher risk of incorrect sorting due to variations in selection criteria based on the workers' experience and expertise, without consistent standards. Moreover, for non-standardized flexible objects with varying sizes and shapes, there can be even greater deviations in the selection criteria. To address these issues, this paper designs a quality inspection system using artificial intelligence-based unsupervised learning methods and conducts research by experimenting with accuracy using a dataset obtained from real manufacturing environments.
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