AI Inference Engine for CAE Redesign Recommendations

The system segments the design improvement process into distinct modular components: FEA simulation module, post-processing module with qualitative result extraction, AI-based inference engine, and design modification module. Each module operates independently with defined interfaces, allowing the complex AI-driven design improvement system to be managed through separate, manageable functional blocks that can be developed and maintained independently.

The system applies local quality by extracting specific qualitative characteristics from different regions of the FEA results based on local structural features and failure modes. Instead of uniformly processing all results, the post-processing module identifies critical areas (such as high-stress regions or potential failure points) and extracts针对性 qualitative descriptions for those specific locations, maintaining precision where needed while achieving generality across different analysis types.

The system performs preliminary action by pre-defining a knowledge base of design patterns, failure modes, and modification strategies before the actual design improvement process. The AI inference engine is pre-trained with engineering knowledge and common design solutions, allowing it to quickly match qualitative analysis results with appropriate modification recommendations without requiring extensive real-time computation, thus reducing processing time while maintaining accuracy.