• Journal of the Korea Safety Management & Science
• /
• v.26 no.2
• /
• pp.23-31
• /
• 2024

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.18 no.3
• /
• pp.283-292
• /
• 2015

This paper presents the development of the automatic safety inspection technique for the ammunition fuzes using radiography images. The technique inspects 49-ammunition fuze by detecting the X-ray or neutron radiographic images to check whether the fuze is unintendedly armed or/and some major assembled parts are at right place. To execute the program, we loads the image(s) for under test. After reading images, the program conducts a series of pre-image processing, and then starts inspecting input images by using the detection algorithms which are designed distinctively for each fuze. After completing the detection process, the program displays the final result of the fuze status: "safety or danger." Through this program, we can cut off the fuzes which have any doubt about safety, and can only provide absolutely safe fuzes, compared with the current naked eye inspection method.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.7
• /
• pp.14-22
• /
• 2016

Because fuses have many parts, human error can occur during visual inspections. This paper proposes an automatic ammunition test algorithm for preventing human error during an inspection. The automatic ammunition test algorithm consists of the following three steps. First, the image input and preprocessing step is where an inspection image is rotated using an image rotation algorithm and the image is converted to a binary image. Second, the inspection step of arming determines if the ammunition is armed using Masked Template Matching algorithm, etc. Third, the inspection step of the parts determines if the parts are omitted using an image searching algorithm, etc. The arming or parts omission of the fuse are detected efficiently using the ammunition automatic test algorithm. The ammunition automatic test algorithm is expected to help improve the safety and reliability of 40 mm grenade fuse.

• Industry Promotion Research
• /
• v.7 no.4
• /
• pp.1-8
• /
• 2022

There are many one-shot devices that are used once and thrown away. One-shot devices such as firecrackers and ammunition are typical, and they are stored for a while after manufacture and then disposed of after use when necessary. However, unlike general operating systems, these one-shot devices have not been properly evaluated. This study first examines what the government does to secure reliability in the case of ammunition through ammunition stockpile reliability program. Next, in terms of statistical analysis, we show what the reliability analysis methods are for one-shot devices such as ammunition. Specifically, we show that it is possible to know the level of reliability if sampling inspection plan such as KS Q 0001 which is acceptance sampling plan by attributes is used. Next, non-parametric and parametric methods are introduced as ways to determine the storage reliability of ammunition. Among non-parametric methods, Kaplan-Meier method can be used since it can also handle censored data. Among parametric methods, Weibull distribution can be used to determine the storage reliability of ammunition.

• Journal of Korean Society for Quality Management
• /
• v.48 no.2
• /
• pp.297-307
• /
• 2020

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.