摘要
Mechanical shock testing is an important criterion for evaluating the reliability of high-power systems. Finite element analysis is often used to reduce power shelf design costs; however, this process becomes time-consuming when multiple design variables are considered. This research proposes an AI-supported structural design workflow that combines FEA, machine learning, and a genetic algorithm to identify multiple optimized design candidates within a short time.