Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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A Study on Determinants of Stockpile Ammunition using Data Mining

데이터 마이닝을 활용한 장기저장탄약 상태 결정요인 분석 연구

  • Roh, Yu Chan (Defense Agency for Technology and Quality) ;
  • Cho, Nam-Wook (Dept. of Industrial Engineering, Seoul National University of Science and Technology) ;
  • Lee, Dongnyok (Department of aerospace engineering, Inha University)
  • 노유찬 (국방기술품질원) ;
  • 조남욱 (서울과학기술대학교 산업공학과) ;
  • 이동녁 (인하대학교 항공우주공학과)
  • Received : 2020.02.05
  • Accepted : 2020.05.07
  • Published : 2020.06.30
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Abstract

Purpose: The purpose of this study is to analyze the factors that affect ammunition performance by applying data mining techniques to the Ammunition Stockpile Reliability Program (ASRP) data of the 155mm propelling charge. Methods: The ASRP data from 1999 to 2017 have been utilized. Logistic regression and decision tree analysis were used to investigate the factors that affect performance of ammunition. The performance evaluation of each model was conducted through comparison with an artificial neural networks(ANN) model. Results: The results of this study are as follows; logistic regression and the decision tree analysis showed that major defect rate of visual inspection is the most significant factor. Also, muzzle velocity by base charge and muzzle velocity by increment charge are also among the significant factors affecting the performance of 155mm propelling charge. To validate the logistic regression and decision tree models, their classification accuracies have been compared with the results of an ANN model. The results indicate that the logistic regression and decision tree models show sufficient performance which conforms the validity of the models. Conclusion: The main contribution of this paper is that, to our best knowledge, it is the first attempt at identifying the significant factors of ASPR data by using data mining techniques. The approaches suggested in the paper could also be extended to other types ammunition data.

Keywords

References

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