This study aims to develop a regression model using data from the Ammunition Stockpile Reliability Program (ASRP) to predict the shelf life of 81mm mortar high-explosive shells. Ammunition is a single-use item that is discarded after use, and its quality is managed through sampling inspections. In particular, shelf life is closely related to the performance of the propellant. This research seeks to predict the shelf life of ammunition using a regression model. The experiment was conducted using 107 ASRP data points. The dependent variable was 'Storage Period', while the independent variables were 'Mean Ammunition Velocity,' 'Standard Deviation of Mean Ammunition Velocity,' and 'Stabilizer'. The explanatory power of the regression model was an R-squared value of 0.662. The results indicated that it takes approximately 55 years for the storage grade to change from A to C and about 62 years to change from C to D. The proposed model enhances the reliability of ammunition management, prevents unnecessary disposal, and contributes to the efficient use of defense resources. However, the model's explanatory power is somewhat limited due to the small dataset. Future research is expected to improve the model with additional data collection. Expanding the research to other types of ammunition may further aid in improving the military's ammunition management system.

In order to prevent accidents via defective ammunition, this paper analyzes recent research on ammunition life prediction methodology. This workanalyzes current shelf-life prediction approaches by comparing the pros and cons of physical modeling, accelerated testing, and statistical analysis-based prediction techniques. Physical modeling-based prediction demonstrates its usefulness in understanding the physical properties and interactions of ammunition. Accelerated testing-based prediction is useful in quickly verifying the reliability and safety of ammunition. Additionally, statistical analysis-based prediction is emphasized for its ability to make decisions based on data. This paper aims to contribute to the early detection of defective ammunition by analyzing ammunition life prediction methodology hereby reducing defective ammunition accidents. In order to prepare not only Korean domestic war situation but also the international affairs from Eastern Europe and Mid East countries, it is very important to enhance the stability of organizations using ammunition and reduce costs of potential accidents.

Domestic 105㎜ HE (High Explosive) shell is composed of three parts that are Fuze, Projectile and Propellants. Among three parts, propelling charge of propellants part consists of single base propellants. It has been known that the lifespan of single base propellants is affected by a storage period. These are because Nitrocellulose (NC) which is the main component of propelling gunpowder can be naturally decomposed to unstable substances similar with other nitric acid ester. Even though it cannot be prevented fundamentally from being disassembled, a decomposition product (NO2, NO3, and HNO3) and tranquillizer DPA (Diphenylamine) having high reactivity are added into a propellant to restrain induction of automatic catalysis by a decomposition product. The decay rate of the tranquillizer is also affected by a production rate of the decomposition product of NC. Therefore, an accurate prediction of the Self-Life is required to ensure against risks such as explosion. Hereupon, this paper presents a new methodology to estimate the shelf-life of single base propellants using data of ASRP (Ammunition Stockpile Reliability Program) to domestic 105mm HE (propelling charge of propellants part). We selected four attributes that are inferred to have influence on distribution of the DPA amount in a propellant from the ASRP dataset through data mining processes. Then the selected attributes were used as independent variables in a regression analysis in order to estimate the shelf-life of single base propellants. 1)