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http://dx.doi.org/10.7469/JKSQM.2020.48.2.297

A Study on Determinants of Stockpile Ammunition using Data Mining  

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)
Publication Information
Journal of Korean Society for Quality Management / v.48, no.2, 2020 , pp. 297-307 More about this Journal
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
Ammunition Stockpile Reliability Program (ASRP); Data Mining; Logistic Regression; Decision Tree; Artificial Neural Networks(ANN);
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Times Cited By KSCI : 3  (Citation Analysis)
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