Life prediction of body tube using fatigue failure model based on genetic algorithm
Konferenz: ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application
17.12.2021 - 19.12.2021 in Shenyang, China
Tagungsband: ICMLCA 2021
Seiten: 5Sprache: EnglischTyp: PDF
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Autoren:
Jiang, Xianjiu; Yang, Li; Chen, Zhaowei; Wei, Jiaxu (Shenyang Ligong University, School of Equipment Engineering, Shenyang, China)
Inhalt:
The longevity of the body tube weapon has been of a great concern, the study of weapon body tubes is becoming a focus of defense research. In this paper, the body tube is the object of study, The reliability of the fatigue life of the body tube is analyzed and the wear of the bore of the body tube is calculated. Monte Carlo sampling was used to simulate the amount of bore wear, and to develop a strain stress model for body tube fatigue damage assessment. A genetic algorithm was used to optimize the parameters of the Weibull distribution to find the optimal confidence interval at 0.95 confidence level. The reliability analysis of the body tube life combined with the test full life data provides theoretical support for the actual body tube launch reliability and launch safety.