Finite Element Results Analysis of UD Fabric Ballistic Impact Based on Machine Learning
Due to the insufficient depth and comprehensiveness of the qualitative comparative observation method currently used to analyze the image results of composite material finite element models,some important information may be overlooked.Therefore,an objective analysis method that can quickly quantify the image is needed.Combined with machine learning,the method of finite element image result analysis based on k-Means clustering algorithm was proposed.Taking the stress distribution results of the ballistic impact finite element model of Dyneema® UD laminate with ordinary aligned stacking and quasi-isotropic stacking as an example,the k-Means clustering algorithm was utilized to cluster and segment the captured stress cloud image by pixel points based on color features.The segmented area enabled fast statistics and area calculations.The results show that this method can efficiently quantify the area difference of different stress ranges,and can obtain more objective and clearer results which is convenient for in-depth analysis.The method can applied to other fields in which cloud image results need to be analyzed.
machine learningUD fabrick-Means clustering algorithmFEA(Finite Element Analysis)stress analysisballistic impact
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