Prediction of stiffness degradation based on machine learning: Axial elastic modulus of [0m /90n ]s composite laminates

This paper, for the first time, proposed an axial elastic modulus degradation prediction method of [0m/90n]s cross-ply laminates using a machine learning (ML) model. The data set of the ML model is established based on the published experiments and a small amount of finite element analysis (FEA) results. The effect of data size on the accuracy of ML prediction is also discussed. The proposed ML model focuses on the process of translating a mechanical problem of damage into a non-linear regression problem of ML, and the mapping between the input and output data, which is hopefully considered for some complex mechanical problems of composites. Meanwhile, the ML method also provides accurate and efficient solution for the engineering practice.

Automated summary

This paper introduces a method for predicting axial elastic modulus degradation of [0m/90n]s cross-ply laminates using a machine learning model, based on experimental data and some finite element analysis results. The study also discusses the impact of data size on the accuracy of ML prediction. The proposed ML model offers an efficient solution for complex mechanical problems of composites.