ML Classification of CAD Parts for Simulation Preparation
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Solution Overview
Problem
In computer-aided design (CAD), analysts spend extensive time identifying and transforming common mechanisms like bolts and springs into simulation-ready forms, which is tedious and error-prone, especially in complex assemblies with hundreds of components.
Innovation Solution
A machine learning-based classification system that uses geometry queries from a CAD kernel to rapidly classify CAD parts into categories, enabling automatic simplification and preparation for Finite Element Analysis (FEA) simulations by training a machine learning model with labeled CAD volumes and applying category-specific reduction operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If analysts manually identify and transform each mechanism in complex CAD assemblies, then preparation accuracy can be maintained, but the time and effort required increases significantly
Solution Approach 1:
The patent replaces manual analyst work with an automated machine learning system. The ML model classifies CAD components into mechanism categories (bolts, springs, gears, etc.) and automatically applies appropriate transformations, substituting human mechanical identification and transformation processes with automated computational processes while maintaining preparation accuracy
Solution Approach 2:
The system enables self-service by allowing the CAD preparation process to perform itself through automated classification and transformation. The machine learning model independently identifies mechanisms and applies category-specific transformations without requiring analyst intervention for each component, significantly reducing preparation time while maintaining accuracy
2Reliability
If analysts manually transform hundreds of components in complex assemblies, then complete coverage can be achieved, but errors increase due to tedium
Solution Approach 1:
The patent replaces manual transformation processes with automated machine learning-based classification and transformation. The system processes hundreds of components through automated ML models that classify each component and apply appropriate transformations, eliminating human error sources while maintaining high processing throughput for complex assemblies
Solution Approach 2:
The system incorporates feedback mechanisms where the ML model learns from classified components and improves its classification accuracy. The automated process provides consistent feedback loops that reduce errors by validating classifications and transformations, ensuring reliable processing of large numbers of components without the fatigue-induced errors of manual work
3Manufacturing precision
If detailed geometric features are preserved in CAD models, then model accuracy is maintained, but processing time for simulation preparation increases
Solution Approach 1:
The patent applies local quality by performing classification and transformation at the component level rather than processing the entire assembly uniformly. The ML model identifies specific mechanism categories (bolts, springs, gears) and applies category-specific transformations only where needed, preserving necessary geometric accuracy for each component type while accelerating overall preparation by processing components locally and independently
Solution Approach 2:
The system segments the complex CAD assembly into individual components and classifies each segment separately using the ML model. This segmentation allows parallel processing of multiple components, maintaining model accuracy for each component while significantly speeding up the overall simulation preparation process through divide-and-conquer methodology
Data Source
AI summary
A computer-implemented method of machine learning classification for Computer Assisted Design (CAD) is provided. The method comprises receiving a number of CAD volumes comprising a dataset, wherein each CAD volume is characterized by a number of scalar values corresponding to features of the CAD volume, wherein the features rely on geometry queries from a CAD kernel. Each CAD volume is labeled with part names according to a set of categories defined by a user, and a machine learning model is trained with the features of the labeled CAD volumes.