Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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A Study on Defect Prediction through Real-time Monitoring of Die-Casting Process Equipment

주조공정 설비에 대한 실시간 모니터링을 통한 불량예측에 대한 연구

  • Chulsoon, Park (Department of Industrial & Systems Engineering, Changwon National University) ;
  • Heungseob, Kim (Department of Industrial & Systems Engineering, Changwon National University)
  • 박철순 (창원대학교 산업시스템공학과) ;
  • 김흥섭 (창원대학교 산업시스템공학과)
  • Received : 2022.11.25
  • Accepted : 2022.12.13
  • Published : 2022.12.31
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Abstract

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

Keywords

Acknowledgement

이 논문은 2021~2022년도 창원대학교 자율연구과제 연구비 지원으로 수행된 연구결과임

References

  1. Abe, N., Zadrozny, B., and Langford, J., Outlier detection by active learning, In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, NY, USA, 2006, pp. 504-509. 
  2. Aggarwal., C.C., A Human-Computer Interactive Method for Projected Clustering, IEEE Transactions on Knowledge and Data Engineering, 2004, Vol.16. No.4, pp. 448-460.  https://doi.org/10.1109/TKDE.2004.1269669
  3. Bouveyron, C., Brunet-Saumard, Camille, Model-based Clustering of High-dimensional Data: A Review, Computational Statistics & Data Analysis, 2014, Vol. 71, pp. 52-78.  https://doi.org/10.1016/j.csda.2012.12.008
  4. Breunig, M.M., Kriegel, Hans-Peter, Ng, R.T., and Sander, J., LOF: Identifying Density-Based Local Outliers, ACM SIGMOD Record, 2000, Vol. 29, Issue 2, pp. 93-104.  https://doi.org/10.1145/335191.335388
  5. Chandola, V., Banerjee, A, Kumar, A., Anomaly detection: A survey, ACM Computing Surveys, 2009, Vol. 41, Issue 3, pp. 1-58.  https://doi.org/10.1145/1541880.1541882
  6. Chawla, N.V., Japkowicz, N., and Kotcz, A., Editorial: special issue on learning from imbalanced data sets, SIGKDD Explorations 6, 2004, 1, pp. 1-6.  https://doi.org/10.1145/1007730.1007733
  7. Choi, E. S., Kim, J.H., Aziz, N., Lee, S.H., Kang, J.T., and Yoo, K.H., Detection of the Defected Regions in Manufacturing Process Data using DBSCAN, The Journal of the Korea Contents Association, 2017, Vol. 17, No. 7, pp. 182-192.  https://doi.org/10.5392/JKCA.2017.17.07.182
  8. Choi, S. and Lee, D., Real-Time Prediction for Product Surface Roughness by Support Vector Regression, Journal of Society of Korea Industrial and Systems Engineering, 2021, Vol. 44, No. 3, pp. 117-124  https://doi.org/10.11627/jkise.2021.44.3.117
  9. Choo, Y.-S. and Shin, S.-J., Cost-optimal Preventive Maintenance based on Remaining Useful Life Prediction and Minimum-repair Block Replacement Models, Journal of Society of Korea Industrial and Systems Engineering, 2022, Vol. 45, No. 3, pp.18-30  https://doi.org/10.11627/jksie.2022.45.3.018
  10. Choi, N.-H., Oh, J.-S., Ahn, J.-R., Kim, K.-.S., A Development of Defect Prediction Model using Machine Learning in Polyurethane Foaming Process for Automotive Seat, Journal of the Korea Academia-Industrial cooperation Society, 2021, Vol. 22, No. 6, pp. 36-42  https://doi.org/10.5762/KAIS.2021.22.6.36
  11. Ezugwu, A.E., Ikotun, A.M., Oyelade, O.O., Abualigah, L., Agushaka, J.O., Eke, C.I., and Akinyelu, A.A., A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects, Engineering Applications of Artificial Intelligence, 2022, Vol. 10, Article 104743. 
  12. Ezugwu, A.E., Shukla, A.K., and Agbaje, M.B., Automatic clustering algorithms: A systematic review and bibliometric analysis of relevant literature, Neural Computing and Applications, 2021, Vol. 33, pp. 6247-6306.  https://doi.org/10.1007/s00521-020-05395-4
  13. Jain, A.K., Data clustering: 50 years beyond K-means, Pattern Recognition Letters, 2010, Vol. 31, Issue 8, pp. 651-666.  https://doi.org/10.1016/j.patrec.2009.09.011
  14. Joshi, M.V., Agarwal, R.C., and Kumar, V., Mining needle in a haystack: Classifying rare classes via two-phase rule induction, In Proceedings of the 2001 ACM SIGMOD international conference on Management of data, ACM Press, New York, NY, USA, pp. 91-102. 
  15. Kim, J., Kang, H.S., and Lee, J.Y., Development of Intelligence Data Analytics System for Quality Enhancement of Die-Casting Process, Journal of the Korean Society for Precision Engineering, 2020, Vol 37, No 4, pp. 247-254.  https://doi.org/10.7736/jkspe.019.136
  16. Kittur, J.K., Manjunath, P.G.C., and Parappagoudar, M.B., Modeling of Pressure die casting process: An Artificial Intelligence Approach, International Journal of Metalcasting, 2015, Vol. 10, Issue 1, pp. 70-87.  https://doi.org/10.1007/s40962-015-0001-7
  17. Kwon, Sehyug, Anomaly Detection of Big Time Series Data Using Machine Learning, Journal of Society of Korea Industrial and Systems Engineering, 2020, Vol. 43, No. 2, pp. 33-38.  https://doi.org/10.11627/jkise.2020.43.2.033
  18. Lee, Jong-Yeong, Choi, Myoung Jin, Joo, Yeongin, Yang, Jaekyung, Ensemble Method for Predicting Particulate Matter and Odor Intensity, Journal of Society of Korea Industrial and Systems Engineering, 2019, Vol. 42, No. 4, pp. 203-210.  https://doi.org/10.11627/jkise.2019.42.4.203
  19. Lee, J.H., Noh, S.D., Kim, H.J., and Kang, Y.S., Implementation of Cyber-Physical Production Systems for Quality Prediction and Operation Control in Metal Casting, Sensors, 2018, Vol. 18, No. 5, 1428. 
  20. Lee, J. and Lee, Y.C., Die-casting fault detection based on unsupervised deep-learning, Proceeding of KSME Annual Meeting, 2021, pp. 1027-1029. 
  21. Lee, S., Lee, S.C, Han, D.S., and Kim, N.S., Study on the Process Management for Casting Defects Detection in High Pressure Die Casting based on Machine Learning Algorithm, Journal of Korea Foundry Society, Vol. 41, No. 6, pp. 521-527.  https://doi.org/10.7777/JKFS.2021.41.6.521
  22. Lee, J.S., Lee, Y.C., and Kim, J.T., Migration from the traditional to the smart factory in the die-casting industry: Novel process data acquisition and fault detection based on artificial neural network, Journal of Material Processing Technology, 2021, Vol. 290, 1735. 
  23. Mac Queen, J.E., Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkley Symposium Math. Stat Prob, 1967, Vol.1, pp. 281-297. 
  24. Mutar, Jinan Redha, A Review of Clustering Algorithms, International Journal of Computer Science and Mobile Applications, 2022, Vol.10, Issue. 10, pp. 44-50. 
  25. Park, C.S. and Bae, S.M., A Study on the Predictive Maintenance of 5 Axis CNC Machine Tools for Cutting of Large Aircraft Parts, Journal of Society of Korea Industrial and Systems Engineering, 2020, Vol. 43, No. 4, pp. 161-167.  https://doi.org/10.11627/jkise.2020.43.4.161
  26. Phua, C., Alahakoon, D., and Lee, V., Minority report in fraud detection: classification of skewed data, SIGKDD Explorer Newsletter 6, 2004, 1, pp. 50-59.  https://doi.org/10.1145/1007730.1007738
  27. Seo, M.-K. and Yun, W.Y., Clustering-based Monitoring and Fault detection in Hot Strip Roughing Mill, Journal of Korean Society for Quality Management, 2017, Vol. 45, No.1, pp. 25-38.  https://doi.org/10.7469/JKSQM.2017.45.1.025
  28. Theiler, J. and Cai, D.M., Resampling approach for anomaly detection in multispectral images, In Proceedings of SPIE 5093, 2003, pp. 230-240. 
  29. Steinwart, I., Hush, D., and Scovel, C., A classification framework for anomaly detection, Journal of Machine Learning Research, 2005, Vol. 6, pp. 211-232. 
  30. Triantafillakis, A., Panagiotis Kanellis, Drakoulis Martakos, Data warehouse clustering on the web, European Journal of Operational Research, 2005, Vol. 160, No. 2, pp. 353-364.  https://doi.org/10.1016/j.ejor.2003.07.012
  31. Ur Rehman, A. and Belhaouari, S.B., Unsupervised outlier detection in multidimensional data, Journal of Big Data, 2021, Vol. 8, p.80 
  32. Vilalta, R. and Ma, S., Predicting rare events in temporal domains, In Proceedings of the 2002 IEEE International Conference on Data Mining, 2002, IEEE Computer Society, Washington, DC, USA, 474.