CAD Model Discretization for Real-Time Manufacturability Prediction
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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 usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human experience is used to predict manufacture appraisals, then flexibility and adaptability are maintained, but accuracy and consistency deteriorate
Solution Approach 1:
The patent introduces machine learning models as an intermediary between human experience and manufacture appraisal predictions. The system trains ML models on historical manufacture data to capture expert knowledge objectively, then uses these models to generate consistent and accurate predictions while maintaining the ability to adapt to new products through continuous learning.
2Adaptability or versatility
If human experience is used to predict manufacture appraisals, then adaptability is maintained, but consistency deteriorates
Solution Approach 1:
The patent replaces the mechanical system of human judgment with an automated machine learning-based system. This substitution eliminates human variability and subjectivity, ensuring that the same input always produces the same output, thereby achieving consistency while maintaining adaptability through the ML model's ability to learn from new data.
3Measurement precision
If standardized methodologies are implemented for manufacture predictions, then accuracy and consistency are improved, but device complexity increases
Solution Approach 1:
The patent segments the manufacture prediction system into distinct modular components: data preprocessing modules, feature extraction modules, multiple specialized ML models for different prediction tasks, and output generation modules. This segmentation allows each component to be independently developed, tested, and maintained, reducing overall system complexity while achieving high accuracy through specialized processing in each module.
4Measurement precision
If machine learning models are implemented for manufacture predictions, then accuracy and consistency are improved, but loss of time in training and deployment increases
Solution Approach 1:
The patent performs preliminary actions by pre-training ML models on extensive historical manufacture data before deployment. The system collects and processes historical data, trains models in advance, and validates performance before actual use. This preliminary preparation ensures high accuracy is achieved beforehand, reducing the time needed during actual prediction operations.
5Manufacturing precision
If detailed discretization of digital models is performed, then manufacturing precision is improved, but computational complexity and time increase
Solution Approach 1:
The patent applies local quality by performing detailed discretization only in critical regions of the digital model that significantly impact manufacture predictions, while using coarser discretization in less critical areas. This selective approach maintains manufacturing precision where it matters most while reducing overall computational complexity and processing time.
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.