Defect Scenario Generation for Synthetic Data
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Solution Overview
Problem
Existing generative models for synthetic data in machine learning often produce unrealistic and implausible defective data samples, lacking control over the characteristics and properties of generated defects, which hinders the effectiveness of machine learning algorithms in inspecting parts for defects.
Innovation Solution
A system comprising a scenario generator unit, a prompt generator unit, a simulator unit, a digital twins data bank unit, and a generative machine learning unit, which generates realistic defective data samples by creating defect scenario data, generating prompts, simulating data based on digital twins, and producing samples that accurately reflect real-world defects with fine control over defect characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing generative models are used to generate synthetic defective data samples, then the generation process is simplified and can be performed automatically, but the generated samples are unrealistic and implausible, failing to accurately represent real-world defects
Solution Approach 1:
The patent introduces an intermediary system comprising a scenario generator, physics engine, and rendering engine between the generative model and the final synthetic defect samples. The scenario generator creates defect scenarios based on real-world physics, the physics engine simulates realistic defect formation processes, and the rendering engine generates photorealistic images. This intermediary chain ensures that while the process remains automated, the generated samples accurately represent real-world defects through physics-based simulation.
2Reliability
If real-world defective samples are collected through intentional manufacturing or production processes, then the data covers actual defect scenarios, but the process is time-consuming, costly, and cannot cover all possible defect varieties
Solution Approach 1:
The patent creates virtual copies of real-world defect scenarios through digital simulation rather than physically manufacturing defective parts. The scenario generator uses real-world defect data to create virtual defect scenarios, the physics engine simulates the physical processes that create these defects, and the rendering engine generates photorealistic images. This copying approach maintains the reliability and accuracy of real defect representation while eliminating the time-consuming and costly process of actual defect manufacturing and collection.
3Productivity
If existing generative models generate synthetic data, then large volumes of data can be produced quickly, but there is lack of control over the characteristics and properties of generated defects
Solution Approach 1:
The patent implements a dynamic scenario generator that allows flexible control over defect characteristics while maintaining high productivity. The scenario generator accepts various input parameters (defect type, location, size, orientation) and dynamically adjusts the simulation parameters accordingly. The physics engine responds to these dynamic inputs by adjusting simulation conditions, and the rendering engine generates images matching the specified characteristics. This dynamic approach enables both high-volume data generation and precise control over defect properties.
Data Source
AI summary
Disclosed are systems and methods for generating realistic defective data samples. An example system for generating realistic defective data samples includes a scenario generator unit configured to generate defect scenario data. The example system includes a prompt generator unit configured to generate prompts based on the defect scenario data. The example system includes a simulator unit configured to generate simulation data based on the defect scenario data and digital twins data. The example system includes a digital twins data bank unit configured to store digital representations of real-world entities and a generative machine learning unit configured to generate a realistic defective data sample based on the simulation data and the prompts.