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