Research on Optimization and Teaching Effectiveness Evaluation of a Transformer-Based Simulation Platform for Mechanical Manufacturing Experiments

To address the limitations of traditional mechanical manufacturing simulation platforms in scene fidelity, teaching adaptability, and effectiveness evaluation, this study proposes an optimized experimental platform integrating Transformer models with a multidimensional assessment framework. Considering that intelligent simulation environments are increasingly important for complex engineering systems and digital training scenarios relevant to electromagnetic equipment and advanced manufacturing, the platform adopts a five-layer architecture incorporating Vision Transformer (ViT), Time-Series Transformer, and BERT models. A high-fidelity virtual scene module is developed to achieve cutting process simulation errors below 5%, while a behavior perception module attains 86.3% accuracy in learning behavior recognition. Personalized recommendation and multidimensional evaluation modules achieve 87.6% consistency with instructor assessments. Teaching experiments involving 236 mechanical engineering students demonstrate that the proposed platform improves participation by 35%, practical skill scores by 18.2%, and innovation design scores by 22.5% compared with conventional methods. The results verify that Transformer-based intelligent simulation effectively enhances teaching quality and adaptive learning capability, providing a practical reference for digital engineering education and simulation-based training frameworks applicable to intelligent manufacturing and electromagnetic system operation environments.