Electric Motor Design Assistance Dynamic Candidate Generation
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Solution Overview
Problem
Conventional design assistance devices for electric motors may fail to select design candidate data with higher evaluation values if such data is not pre-stored in the system.
Innovation Solution
The design assistance device acquires and evaluates multiple design candidate data sets, selects top-performing data, generates new candidate data sets based on these selections, and recalculates evaluation values to ensure the selection of the highest-evaluation data for electric motor design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If design candidate data is pre-stored in the storage unit, then the calculating unit can perform evaluation and selection, but the system cannot select design candidate data with higher evaluation values that were not pre-stored
Solution Approach 1:
The system dynamically generates new design candidate data by modifying existing design candidate data from the storage unit, rather than relying solely on pre-stored data. This dynamic generation process creates new design candidates that were not previously in the database, enabling the system to evaluate and select designs with higher evaluation values while maintaining the ability to perform systematic evaluation through the calculating unit.
2Device complexity
If the storage unit contains a finite number of design candidate data, then the system structure remains manageable, but the calculating unit cannot find design candidate data with higher evaluation values
Solution Approach 1:
The design candidate data in the storage unit is segmented into multiple groups, and new design candidate data is generated by modifying individual groups or combinations of groups. This segmentation approach allows the system to maintain manageable storage while systematically exploring design spaces to generate new candidates with potentially higher evaluation values, balancing data storage complexity with evaluation accuracy.
Solution Approach 2:
The system performs preliminary modification of existing design candidate data to generate new design candidate data before final evaluation. By pre-processing and modifying design parameters in advance, the system expands the available design space without requiring proportional increases in storage capacity, enabling more accurate selection of high-evaluation designs.
Data Source
AI summary
A design assistance method includes: acquiring design candidate data including design parameters as electric motor design candidates, and acquiring a first evaluation value of each piece of design candidate data; selecting top at least one piece of design candidate data having the first evaluation value relatively high as first design candidate data, and generating second design candidate data including the design parameters from the first design candidate data; calculating a second evaluation value of the first design candidate data from the design parameters included in the first design candidate data, and calculating the second evaluation value of the second design candidate data from the design parameters included in the second design candidate data; and selecting design candidate data to be used as design data of the electric motor from among the first design candidate data and the second design candidate data from the second evaluation value.