Conditional GAN Training with Embedded Physics for Component Screening
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Solution Overview
Problem
Existing AI systems struggle to effectively utilize image recognition for engineering design and manufacturing due to the inability to directly apply engineering analysis methods to component images, leading to inefficient use of artificial intelligence in assessing component performance and design.
Innovation Solution
Training a Conditional Generative Adversarial Network (cGAN) with embedded graphical encodings of physical parameters in component images to discriminate between acceptable and non-acceptable components, using glyphs and histograms to facilitate faster and more accurate neural network training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI systems use image recognition for engineering design and manufacturing, then functionality and performance can be improved, but the systems cannot directly apply engineering analysis methods to component images
Solution Approach 1:
The patent combines image recognition technology with engineering analysis methods by integrating finite element analysis (FEA) results directly into the training dataset. The system merges visual component images with corresponding engineering analysis data, allowing the AI to simultaneously process both image features and engineering parameters, thereby resolving the contradiction between improving reliability and managing system complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts engineering analysis results into image-compatible formats. This intermediary layer transforms complex engineering data into visual representations that can be integrated with image recognition workflows, enabling the AI system to utilize engineering analysis methods without requiring direct integration of complex computational engines.
2Measurement precision
If computational methods are used to fully evaluate component performance, then accurate assessment is achieved, but the process becomes time-consuming and computationally intensive
Solution Approach 1:
The patent performs preliminary computational analysis during the training phase by pre-computing engineering analysis results for all training images. This preliminary action creates a comprehensive dataset that already contains evaluated performance metrics, allowing the AI system to make rapid predictions during deployment without performing time-consuming computational analyses in real-time, thus resolving the contradiction between measurement precision and time loss.
3Productivity
If standard image recognition methods are used without embedded physical parameters, then processing is simpler, but the AI system cannot accurately assess component performance based on design criteria
Solution Approach 1:
The patent adds another dimension to standard image recognition by embedding physical parameter information directly into the image data. This is achieved by overlaying or integrating engineering analysis results, material properties, and performance metrics onto component images, transforming them from purely visual data to multi-dimensional data that includes both visual and physical characteristics, enabling accurate performance assessment while maintaining processing efficiency.
Data Source
AI summary
Methods and techniques are disclosed for training Conditional Generative Adversarial Networks using images of acceptable and non-acceptable components. Methods and techniques are also disclosed for using Conditional Generative Adversarial Networks to assess the acceptability or non-acceptability of a component using an image thereof.