Multi-objective optimization of a bistable curved shell with controllable thickness based on machine learning

Abstract

Bistable curved shells have become a promising low-cost application in energy absorption fields owing to recent advances in material and technology. Significant research has been conducted to improve their energy absorption effect through forward prediction and single-objective optimization. However, these approaches may not fully explore their functional potential. In this study, we propose a multi-objective optimization framework based on the principle of main objective optimization that combines neural networks and genetic algorithms. The energy absorption effect and backward snapping force of the bistable curved shell are improved synchronously. Meanwhile, a reverse design algorithm is developed to generate the preset load-displacement curve, which further expands the application of machine learning methods in the field of multi-objective optimization. The combination of machine learning and multi-objective optimization is highly effective for building meta-structures with specific performance requirements and has potential applications in solving complex optimization tasks in various fields.

Keywords

• Bistable curved shell
• Structure optimization
• Machine learning
• Multi-objective optimization
• Reverse design