Research on Mechanical Sensitivity Response Prediction of Explosives Based on Machine Learning

In order to reduce the workload and uncertainty of conventional mechanical sensitivity tests of explosives, and to obtain the performance parameters of explosives more quickly and accurately, machine learning methods to predict the response values of explosives mechanical sensitivity is proposed. Th...

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Bibliographic Details
Published in2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) pp. 175 - 179
Main Authors Yao, Fanghao, Bu, Xiongzhu, Ding, Liangzheng, Huang, Xinhao, Zhang, Zouzou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2022
Subjects
Data models
explosive
Explosives
Machine learning
Predictive models
QMU evaluation
safety
Sensitivity
Training
Uncertainty
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Summary:In order to reduce the workload and uncertainty of conventional mechanical sensitivity tests of explosives, and to obtain the performance parameters of explosives more quickly and accurately, machine learning methods to predict the response values of explosives mechanical sensitivity is proposed. The dataset is constructed by real test, finite element simulation and Monte Carlo data enhancement. By training and hyperparameter tuning for different classification models, we conclude that BP neural network is the best. The model is tested with real test data, and the results show that it is effective and feasible to predict the response values of mechanical sensitivity. It also provides a good reference for multiple QMU (quantification of margins and uncertainties) reliability evaluation of explosives.
DOI:10.1109/AEMCSE55572.2022.00043