Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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Analysis of Machine Learning Research Patterns from a Quality Management Perspective

품질경영 관점에서 머신러닝 연구 패턴 분석

  • Ye-eun Kim (Department of Industrial Engineering, Sungkyunkwan University) ;
  • Ho Jun Song (Department of Industrial Engineering, Sungkyunkwan University) ;
  • Wan Seon Shin (Department of Systems Management Engineering, Sungkyunkwan University)
  • 김예은 (성균관대학교 산업공학과) ;
  • 송호준 (성균관대학교 산업공학과) ;
  • 신완선 (성균관대학교 시스템경영공학과)
  • Received : 2024.02.01
  • Accepted : 2024.03.06
  • Published : 2024.03.31
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Abstract

Purpose: The purpose of this study is to examine machine learning use cases in manufacturing companies from a digital quality management (DQM) perspective and to analyze and present machine learning research patterns from a quality management perspective. Methods: This study was conducted based on systematic literature review methodology. A comprehensive and systematic review was conducted on manufacturing papers covering the overall quality management process from 2015 to 2022. A total of 3 research questions were established according to the goal of the study, and a total of 5 literature selection criteria were set, based on which approximately 110 research papers were selected. Based on the selected papers, machine learning research patterns according to quality management were analyzed. Results: The results of this study are as follows. Among quality management activities, it can be seen that research on the use of machine learning technology is being most actively conducted in relation to quality defect analysis. It suggests that research on the use of NN-based algorithms is taking place most actively compared to other machine learning methods across quality management activities. Lastly, this study suggests that the unique characteristics of each machine learning algorithm should be considered for efficient and effective quality management in the manufacturing industry. Conclusion: This study is significant in that it presents machine learning research trends from an industrial perspective from a digital quality management perspective and lays the foundation for presenting optimal machine learning algorithms in future quality management activities.

Keywords

References

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