DOI QR코드

DOI QR Code

A Decision Tree Approach for Identifying Defective Products in the Manufacturing Process

  • Received : 2017.05.02
  • Accepted : 2017.06.19
  • Published : 2017.06.28

Abstract

Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect.

Acknowledgement

Grant : Development of Predictive Manufacturing System using Data Analysis of 4M Data in Small and Medium Enterprise

Supported by : Ministry of Trade, Industry & Energy (MI), IITP(Institute for Information & Communication Technology Promotion)

References

  1. Ian H. Witten and Eibe Frank, "Data Mining: Practical machine learning tools and techniques," Morgan Kaufmann, 2005.
  2. K. Wang, "Applying data mining to manufacturing: the nature and implications," Journal of Intelligent Manufacturing, vol. 18, no. 4, 2007, pp. 487-495. https://doi.org/10.1007/s10845-007-0053-5
  3. A. Kusiak, "Data mining: manufacturing and service applications," International Journal of Production Research, vol. 44, no. 18-19, 2006, pp. 4175-4191. https://doi.org/10.1080/00207540600632216
  4. L. Rokach and O. Maimon, "Data mining for improving the quality of manufacturing: a facture set decomposition approach," Journal of Intelligent Manufacturing, vol. 17, no. 3, 2006, pp. 285-299. https://doi.org/10.1007/s10845-005-0005-x
  5. Mark A. Friedl and Carla E. Brodley, "Decision tree classification of land cover from remotely sensed data," Remote sensing of environment, vol. 61, no. 3, 1999, pp. 399-409. https://doi.org/10.1016/S0034-4257(97)00049-7
  6. J. Ross Quinlan, C4.5: programs for machine learning, Elsevir, 1993.
  7. C. Y. Jae, "Leverage big data technology in manufacturing," Journal of the Koreand Institute of Communication Sciences, vol. 29, no. 11, 2012, pp. 30-35.
  8. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: an update," ACM SIGKDD explorations newsletter, vol. 11, no. 1, 2009, pp. 10-17. https://doi.org/10.1145/1656274.1656278