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
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 209 - 220 (12page)
- DOI
- 10.33162/JAR.2024.6.24.2.209
이용수
초록· 키워드
오류제보하기
Purpose: This study aims to develop an artificial intelligence (AI) model that predicts product quality by considering the interaction of various process parameters and controlling them to ensure the optimal combination of process variables.
Methods: We developed a production quality prediction model using machine learning and particle swarm optimization (PSO). Based on this model, we explored optimal process conditions that maximize the likelihood of producing high-quality products.
Results: By evaluating the quality prediction (classification) performance of various machine learning candidate models, we achieved an accuracy of over 95%. Using this model, we formulated an optimization problem to identify optimal process conditions.
Conclusion: The selected production quality prediction model was based on a support vector machine (SVM) model enhanced with the synthetic minority over-sampling technique (SMOTE). A process control chart was developed using the PSO algorithm to identify optimal process conditions. This approach enables the intelligent control of the electroplating process, thus improving production quality.
Methods: We developed a production quality prediction model using machine learning and particle swarm optimization (PSO). Based on this model, we explored optimal process conditions that maximize the likelihood of producing high-quality products.
Results: By evaluating the quality prediction (classification) performance of various machine learning candidate models, we achieved an accuracy of over 95%. Using this model, we formulated an optimization problem to identify optimal process conditions.
Conclusion: The selected production quality prediction model was based on a support vector machine (SVM) model enhanced with the synthetic minority over-sampling technique (SMOTE). A process control chart was developed using the PSO algorithm to identify optimal process conditions. This approach enables the intelligent control of the electroplating process, thus improving production quality.
목차
1. 서론
2. 데이터 전처리 및 차원축소
3. 생산품질 예측 모형
4. 공정 조건 최적화
5. 결론
References
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