Design Support Device for CAD Rule Threshold Quantification
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Solution Overview
Problem
Existing design support technologies require designers to manually designate ambiguous design rule thresholds, which is time-consuming and labor-intensive, especially as machine tools, materials, and design trends evolve over time.
Innovation Solution
A design support device that registers normal and violation CAD data, calculates statistical values to determine the influence degree of each parameter, and presents a scatter diagram for cluster analysis to help users quantify ambiguous design rules by statistically extracting parameter ranges and visualizing thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If designers manually designate thresholds of parameters for design rules, then the design rules can be established, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs self-learning by automatically analyzing CAD data and determining design rule thresholds without requiring manual designer input. The learning unit acquires multiple CAD data, determines parameters, and automatically sets thresholds through statistical analysis, enabling the system to serve itself rather than relying on continuous human intervention.
Solution Approach 2:
The system performs preliminary learning by acquiring and analyzing CAD data in advance to establish design rules before actual design evaluation. The learning unit pre-determines parameters and thresholds by statistically analyzing accumulated CAD data, so that when design rules are needed for evaluation, they are already established and ready for use.
2Adaptability or versatility
If design rules are continuously reviewed and updated to reflect changing machine tools, materials, and trends, then the design rules remain current, but the process becomes very time-consuming and labor-intensive
Solution Approach 1:
The system establishes feedback loops where evaluation results and new CAD data are fed back into the learning unit. The learning unit continuously acquires new CAD data, re-analyzes parameters, and updates thresholds based on accumulated data, creating a self-improving system that automatically adapts to changing design trends and requirements.
Solution Approach 2:
The design rules are made dynamic rather than static. The thresholds are not fixed but are continuously updated based on statistical analysis of accumulated CAD data. The system can adapt its parameters and thresholds over time to reflect changing machine tools, materials, and design trends without manual intervention.
3Measurement precision
If inexperienced designers quantify thresholds by comparing with past models or interviewing veteran designers, then the thresholds can be established, but the process becomes troublesome and inefficient
Solution Approach 1:
The system replaces the mechanical process of human comparison and interview-based threshold determination with an automated computational system. The learning unit uses statistical analysis algorithms to automatically determine thresholds from CAD data, substituting human cognitive processes with machine-based data analysis that is both accurate and efficient.
Data Source
AI summary
A design support device includes a unit that accepts registration of normal CAD data with a past record and violation CAD data determined to have violated portion with respect to three-dimensional CAD data, a unit that acquires a parameter related to a design rule from CAD data to be evaluated, a unit that calculates a statistical value of the acquired parameter and calculates a value of an influence degree t that explains a violation of each parameter, and a unit that presents a scatter diagram plotting normal CAD data and violation CAD data to be evaluated and a boundary line subjected to cluster analysis, on a two-dimensional coordinate system, according to a combination of parameters designated by a user (evaluator).