3D CAD Training Data Conversion for Precise Shape Generation
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Solution Overview
Problem
Conventional generative AI techniques struggle to accurately recognize and generate three-dimensional shapes, particularly complex 3D CAD data, leading to vagueness in recognition and inability to implement precise three-dimensional modeling.
Innovation Solution
A method involving the conversion of CAD data to a predetermined protocol, such as STEP AP242, to generate training data that allows AI to accurately learn and generate three-dimensional models by associating parts with precise instructions through language expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional generative AI techniques are used to learn three-dimensional shapes from visual information, then the AI can process images and videos, but the recognition accuracy of three-dimensional shapes remains vague and insufficient for complex 3D CAD data
Solution Approach 1:
The patent creates a standardized protocol that copies and represents complex 3D CAD data in a simplified, structured format. This protocol captures essential geometric and topological information while discarding unnecessary complexity, enabling AI systems to process and learn from 3D shape data with high accuracy without being overwhelmed by the full complexity of original CAD files
Solution Approach 2:
The patent transforms 3D CAD data by changing its representation parameters from unstructured visual information to a standardized protocol with specific geometric and topological parameters. This parameter transformation enables AI to systematically learn and recognize three-dimensional shapes by processing structured data with defined attributes rather than raw visual input
2Manufacturing precision
If AI learns three-dimensional shapes from visual information only, then image processing is possible, but it cannot accurately understand and generate complex 3D CAD data with precise instructions
Solution Approach 1:
The patent introduces a standardized protocol as an intermediary between complex 3D CAD data and AI processing. This protocol acts as a mediator that translates complex CAD data into a structured format with defined geometric and topological parameters, enabling AI to accurately understand and generate three-dimensional models with precise instructions without directly handling the full complexity of original CAD data
Solution Approach 2:
The patent segments 3D CAD data into distinct geometric and topological components that can be independently processed and learned by AI. By dividing complex CAD data into structured protocol elements with specific parameters, the system enables precise control and accurate reconstruction of three-dimensional shapes while reducing processing complexity
Data Source
AI summary
A method for generating training data for learning a three-dimensional shape of a recognition target includes: acquiring CAD data of the three-dimensional shape by a 3D CAD data acquisition unit; and converting the CAD data of the three-dimensional shape acquired by the 3D CAD data acquisition unit to STEP AP242 by a language expression conversion unit 3 to generate training data of the three-dimensional shape. It is therefore possible to implement a generative AI technique for 3D CAD that can understand, with high accuracy, even complex 3D CAD data and generate a three-dimensional model according to precise instructions.