CAD Model Discretization for Real-Time Manufacturability Prediction
Solution Overview
Problem
Manufacturing companies face inaccuracies and inconsistencies in predicting production times and costs due to reliance on human experience without standardized methodologies, leading to increased overhead and a lack of real-time objective appraisal tools for products specified in CAD models.
Innovation Solution
A computer-based method using machine learning and artificial intelligence to discretize digital models, infer manufacture processes, and provide near real-time predictions by determining physical attributes and applying symbolic functions to generate axioms for manufacturing processes, optimizing predictions for production times, costs, and material selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human experience is used for predicting manufacture appraisals, then subjective judgment is applied, but accuracy and consistency deteriorate
Solution Approach 1:
The patent replaces the mechanical system of human expert judgment with an automated computer-based machine learning system. The system uses trained models to predict manufacture appraisals, eliminating subjective human variation while maintaining standardized methodology. This substitution directly addresses the contradiction by improving reliability through automation without requiring complex human expertise.
Solution Approach 2:
The patent transforms qualitative human experience into quantitative parameters that can be processed by machine learning models. By converting manufacture appraisal criteria into measurable parameters and using trained models to process them, the system achieves consistent, accurate predictions without relying on subjective human judgment, thereby resolving the reliability-complexity contradiction.
2Stability of the object's composition
If standardized methodologies are implemented, then consistency is improved, but implementation complexity increases
Solution Approach 1:
The patent implements a self-service system where the machine learning models automatically perform prediction tasks without requiring external standardized methodology implementation. The trained models inherently encode consistency rules, eliminating the need for separate standardization procedures. This approach maintains prediction consistency while avoiding the complexity of implementing and maintaining external standardized systems.
Solution Approach 2:
The patent replaces the mechanical process of implementing standardized methodologies with an automated machine learning system. The models are trained once to embody consistency principles, then automatically apply them without requiring ongoing standardization management. This substitution achieves stable, consistent predictions while reducing the operational complexity of maintaining standards.
3Speed
If real-time predictions are provided, then decision-making speed is improved, but computational requirements increase
Solution Approach 1:
The patent performs preliminary action by training machine learning models in advance with comprehensive manufacture data. The trained models capture complex patterns and relationships during the training phase, enabling them to make rapid predictions during deployment. This preliminary training investment reduces the computational energy required for real-time predictions, as the models perform inference rather than full analysis during actual use.
Solution Approach 2:
The patent creates simplified copies of complex manufacture processes through trained machine learning models. Instead of performing full computational analysis for each prediction, the system uses the trained model copies that have already learned the essential patterns. These model copies enable fast, energy-efficient real-time predictions while maintaining accuracy, resolving the speed-energy contradiction.
Data Source
AI summary
The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.