DOI: 10.1177/07316844241228182 ISSN: 0731-6844

A phase field and machining-learning approach for rapid and accurate prediction of composites failure

Jin Gao, Yuelei Bai, Xiaodong He, Haolong Fan, Guangping Song, Xiaocan Zou, Zhenqian Xiao, Yongting Zheng
  • Materials Chemistry
  • Polymers and Plastics
  • Mechanical Engineering
  • Mechanics of Materials
  • Ceramics and Composites

A new approach is proposed for rapid and accurate prediction for composite failure in combination of the phase field and machine-learning methods. First, using experimentally-fitted tangent modulus instead of elastic modulus as constitutive relationship, a modified phase field method (MPFM) is established for the crack propagation and mechanical response, which can be effectively applied for composites with a nonlinear constitutive relationship. Interestingly, both the crack propagation path and mechanical responses of two typical examples of composites using MPFM are well consistent with previously available experimental and calculated ones. Furthermore, the data-driven back propagation neural network (BPNN) is constructed to greatly accelerate the prediction on a database generated by MPFM, emphasizing several critical parameters, for example, fiber orientations, external load, maximum failure strain, and critical strain energy release rate. Of much importance, the well-trained BPNN builds a bridge between the traditional computational fracture mechanics and machine learning algorithms, enabling non-specialists to accurately calculate the mechanical response of composites, moreover, saving over 99% of computing time in comparison with MPFM.

More from our Archive