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.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2025.4
- 수록면
- 561 - 564 (4page)
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초록· 키워드
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The casting process is a manufacturing method where molten metal is poured into a mold and solidified, making it ideal for mass production with precise shape formation. However, traditional casting relies heavily on operator experience, leading to inconsistent quality. While deep learning-based defect prediction has gained attention, the black box nature of AI models limits interpretability, reducing operator trust. To address this, we propose a deep learning-based Multi-Role AI Model that integrates three key functions: (1) Pass/Fail prediction, (2) defect cause analysis using Explainable AI (XAI), and (3) optimal process variable exploration. Experimental results show 98.7% accuracy in defect prediction, effective identification of defect causes, and optimized process parameters that minimize defects and reduce costs. This study enhances fault prediction reliability and process efficiency in casting through AI-driven optimization.
목차
Abstract
1. 서론
2. 제안 방법
3. 검증
4. 결론
Abstract
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