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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.8
- 수록면
- 406 - 413 (8page)
- DOI
- 10.7232/JKIIE.2021.47.4.406
이용수
초록· 키워드
오류제보하기
Ultraviolet-curable coating is environmentally friendly and has the advantage of reducing the process time. The UV-curable coating is mainly applied to interior and exterior parts of automotive vehicles. In particular, adhesion is an important factor in the quality control of the automotive manufacturing process to improve the product reliability. Therefore, human being experts make an effort of testing the adhesion defect in the automotive manufacturing process. However, there is a disadvantage that the test result is subjective and costly because the engineer manually performs the adhesion test. In this study, the risk level of adhesion defect is quantified to predict the adhesion defect in a UV curable coating process. XGBoost is employed to predict the adhesion defect of a product and quantify the corresponding risk level in an automotive manufacturing process.
목차
1. 서론
2. 자외선 경화형 도장의 부착성 불량예측 모델
3. 실험과 평가
4. 결론과 향후 연구
참고문헌
참고문헌 (28)
참고문헌 신청
Boyle, P. P. (1977), Options : A Monte Carlo Approach, Journal of Financial Economics, 4(3), 323-338.
Changyong, F. E. N. G., Hongyue, W. A. N. G., Naiji, L. U., Tian, C. H. E. N., Hua, H. E., and Ying, L. U. (2014), Log-Transformation and its Implications for Data Analysis, Shanghai Archives of Psychiatry, 26(2), 105.
Chen, K., Chen, H., Liu, L., and Chen, S. (2019), Prediction of Weld Bead Geometry of MAG Welding based on XGBoost Algorithm, The International Journal of Advanced Manufacturing Technology, 101(9-12), 2283-2295.
Chen, T. and Guestrin, C. (2016), Xgboost : A Scalable Tree Boosting System, In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
Chu, B.-H. (2020), Optimal Level Combination Analysis of Factors Affecting the Adhesion of Ultraviolet Curing Coating and Development of a Model for Prediction of Adhesion Fault(Masters dissertation), Hanyang University, Korea, 1-18.
Changyong, F. E. N. G., Hongyue, W. A. N. G., Naiji, L. U., Tian, C. H. E. N., Hua, H. E., and Ying, L. U. (2014), Log-Transformation and its Implications for Data Analysis, Shanghai Archives of Psychiatry, 26(2), 105.
Chen, K., Chen, H., Liu, L., and Chen, S. (2019), Prediction of Weld Bead Geometry of MAG Welding based on XGBoost Algorithm, The International Journal of Advanced Manufacturing Technology, 101(9-12), 2283-2295.
Chen, T. and Guestrin, C. (2016), Xgboost : A Scalable Tree Boosting System, In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
Chu, B.-H. (2020), Optimal Level Combination Analysis of Factors Affecting the Adhesion of Ultraviolet Curing Coating and Development of a Model for Prediction of Adhesion Fault(Masters dissertation), Hanyang University, Korea, 1-18.
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