Accelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning Models

Polymers (Basel). 2022 Aug 8;14(15):3229. doi: 10.3390/polym14153229.

Abstract

Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems.

Keywords: composite laminate; finite element simulation; layup design; mechanical property; neural network.

Grants and funding

This research was funded by the University Stability Support Program Project of the Shenzhen Natural Science Foundation (20200814105851001), the National Key R&D Program of China (2018YFB2100901) and the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone (HZQB-KCZYB-2020083). The work was also partially supported by the Induction of Entrepreneurship Talents Program funded by Foshan-HKUST Projects (Grant #: FSUST20-ETP06).