An efficient deep learning-based topology optimization method for continuous fiber composite structure

Jicheng Li

doi:10.1007/s10409-024-24207-x

Return

An efficient deep learning-based topology optimization method for continuous fiber composite structure

2024-09-04

PRE

DOI：10.1007/s10409-024-24207-x

Original

Original request for help

Save

Abstract

En 中文

This paper presents a deep learning-based topology optimization method for the joint design of material layout and fiber orientation in continuous fiber-reinforced composite structure (CFRCS). The proposed method mainly includes three steps: (1) a ResUNet-involved generative and adversarial network (ResUNet-GAN) is developed to establish the end-to-end mapping from structural design parameters to fiber-reinforced composite optimized structure, and a fiber orientation chromatogram is presented to represent continuous fiber angles; (2) to avoid the local optimum problem, the independent continuous mapping method (ICM method) considering the improved principal stress orientation interpolated continuous fiber angle optimization (PSO-CFAO) strategy is utilized to construct CFRCS topology optimization dataset; (3) the well-trained ResUNet-GAN is deployed to design the optimal structural material distribution together with the corresponding continuous fiber orientations. Numerical simulations for benchmark structure verify that the proposed method greatly improves the design efficiency of CFRCS along with high design accuracy. Furthermore, the CFRCS topology configuration designed by ResUNet-GAN is fabricated by additive manufacturing. Compression experiments of the specimens show that both the stiffness structure and peak load of the CFRCS topology configuration designed by the proposed method have significantly enhanced. The proposed deep learning-based topology optimization method will provide great flexibility in CFRCS for engineering applications.

Keywords:

Topology optimization

Fiber-reinforced composite structure

Generative and adversarial networks

Additive manufacturing