Fatigue Optimization of Sell Extractor Skateboard in a High-firing-speed Automatic Gun Based on Kriging Model
In order to improve the fatigue life of shell extractor skateboard and meet the minimum life requirement of a certain 10-shot-high-firing-speed automatic gun with a firing rate of around 1 500 shots per minute up to 1 000 rounds,a surrogate model based on Kriging regression is proposed for the fatigue optimization of shell extractor skateboard.On the basis of the finite element model consistent with the experimental results,the initial sample points are set by Latin hypercube sampling,a structural model corresponding to the sample points is constructed,and the theoretical life of each group of samples is calculated.A Kriging surrogate model is constructed according to the initial sample points,and the surrogate optimization algorithm,which takes the expected improvement(EI)criterion as the addition point criterion and the genetic algorithm as the sub-optimization algorithm,is used to optimize the objective function.The fatigue life of shell extractor skateboard is increased to 1193 rounds after optimization,and the optimized result meets the tactical technical index of the automatic gun after experimental verification.The research results show that the surrogate optimization algorithm based on Kriging and genetic algorithm can quickly and effectively find the global optimal solution,which is applicable to the fatigue optimization of broken parts in the high-firing-speed automatic gun,and has certain reference significance for engineering applications.
finite element simulationfatigue analysisKriging regressionexpected improvement criteriongenetic algorithm
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维普
