AI Advisory System for CAE Design Modification
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Solution Overview
Problem
Current computer-aided engineering (CAE) systems lack effective use of artificial intelligence (AI) in the post-processing phase and design modification of engineering products, particularly in finite element analysis (FEA), limiting the ability to extract meaningful qualitative design information for design improvements.
Innovation Solution
A rule-based suggestion engine that utilizes a knowledge base and inference engine to analyze user-designed objects, identify design flaws, and suggest modifications by comparing them to previously created objects with desired physical properties, allowing users to choose and implement design paths that achieve specific performance enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI methods are applied to post-processing phase and design modification, then design improvement capability is enhanced, but system complexity increases
Solution Approach 1:
The patent introduces an independent intelligent advisory system that acts as an intermediary between the FEA analysis results and the design modification process. This separate module receives analysis results, extracts meaningful qualitative design information, and provides design improvement suggestions without integrating AI deeply into the core FEA engine, thus enhancing design capability while controlling system complexity
Solution Approach 2:
The system divides the design improvement functionality into separate modular components: the FEA analysis module, the intelligent advisory module, and the design modification module. This segmentation allows the AI-based design improvement capability to be added as a distinct layer that processes analysis results independently, avoiding complexity proliferation in the entire system
2Adaptability or versatility
If qualitative description of engineering analysis results is used, then system generality and applicability are improved, but information precision may be reduced
Solution Approach 1:
The system transforms quantitative FEA analysis results into qualitative design information by changing the parameter representation from numerical values to descriptive categories. This allows the system to handle diverse engineering problems generally while maintaining actionable precision through structured qualitative descriptors that capture essential design insights
Data Source
AI summary
A system or method includes receiving data regarding a first simulated object with at least one desired physical property to be exhibited by the first simulated object designed by a user. The method includes receiving a request for modifications to the simulated object to achieve the at least one desired physical property and determining based on the at least one desired physical property other simulated objects designed by other users, the other simulated objects exhibit the desired physical properties. The display at least one design path that shows other simulated objects that has the desired physical properties and allowing the user choose one of the other simulated objects and replace the object with the chosen object.