Modal-Constrained Co-optimization Approach of Thermomechanical Performance and Weight for Straight-Ribbed Brake Discs Using Deep Learning

In this study, an efficient method for concurrent thermomechanical performance and weight optimization under modal constraints is proposed to address the coupled design challenges of thermomechanical characteristics (thermal capacity, thermal deformation, and modal) and structural weight in straight-ribbed brake discs. Based on high-fidelity computer-aided engineering (CAE) simulations of brake disc thermomechanical behavior, a neural network (NN)-based surrogate model and a ResNet-guided geometric feature recognition (RGFG) model for automatic modality recognition were developed, and integrated with a particle swarm optimization (PSO) framework for optimal solution exploration. When applied to a passenger vehicle brake disc case study, the surrogate model of NN demonstrates remarkable accuracy: it shows more than 95% agreement with the CAE results in thermal capacity prediction, the prediction accuracy of thermal deformation exceeds 90% compared to CAE results and 83.4% compared to test result, thereby validating the method’s effectiveness. Compared with conventional CAE approaches, the surrogate model of NN achieves a subsecond prediction speed, significantly reducing computational costs. The surrogate model of RGFG achieves a test accuracy exceeding 95%. Furthermore, the proposed optimization framework offers valuable insights for the inverse design of brake discs.