Neural Network Material Structure Generation and Analysis
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Solution Overview
Problem
Generating new material structures is a time-consuming and expensive process typically performed manually through experimentation and finite element analysis, making it difficult to quickly develop and test new materials.
Innovation Solution
A material generation system that uses machine learning models to analyze and generate new material structures by modifying initial structures, determining stress and strain values, and updating the model based on finite element analysis results, allowing for rapid evaluation and improvement of material properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual experimentation and finite element analysis are used to generate new material structures, then the accuracy and reliability of material analysis is improved, but the time required and cost increase significantly
Solution Approach 1:
The patent uses machine learning models to create virtual copies of physical material structures that can be analyzed and modified digitally. The system generates synthetic material structures from initial structures by adding, removing, or modifying portions, creating virtual replicas that can be evaluated through finite element analysis without requiring physical prototyping for each variation, thus reducing time while maintaining accuracy
Solution Approach 2:
The system implements an iterative feedback loop where machine learning models analyze generated material structures, provide evaluations of their properties, and the results feed back into generating improved initial structures. This feedback mechanism allows the system to progressively improve material structure generation accuracy while reducing the need for time-consuming manual experimentation
2Reliability
If manual experimentation is used to develop new materials, then the reliability of material development is improved, but the productivity decreases
Solution Approach 1:
The system performs preliminary actions by using machine learning models to pre-evaluate and rank potential material structures before physical experimentation. The model analyzes initial structures and generates modified versions with predicted properties, allowing researchers to prioritize the most promising candidates for physical testing, thus increasing productivity while maintaining reliability through targeted experimentation
Solution Approach 2:
The patent creates virtual copies of material structures that can be rapidly iterated and evaluated through computational methods. By working with digital replicas rather than physical materials for each iteration, the system enables rapid exploration of multiple material configurations while maintaining the reliability of physical material development through targeted validation
3Measurement precision
If traditional methods are used to generate and test material structures, then the accuracy of material property determination is improved, but the cost increases
Solution Approach 1:
The system creates virtual copies of material structures that can be analyzed through computational methods rather than requiring physical resources for each test. The machine learning model generates and evaluates multiple material structure variations digitally, reducing the quantity of physical materials needed while maintaining measurement precision through accurate computational analysis
Solution Approach 2:
The system changes parameters by using machine learning models to predict material properties based on structural characteristics rather than requiring physical measurement for each property. This allows accurate determination of multiple material properties through computational parameter analysis rather than consuming physical resources for each measurement
Data Source
AI summary
In one embodiment, a method is provided. The method includes obtaining a set of graphs for a set of material structures. Each graph of the set of graphs is associated with a material structure of the set of material structures. The method also includes determining first sets of stress values and first sets of strain values for the set of graphs based on a neural network. The method further includes obtaining second sets of stress value and second sets of strain values for a subset of the set of material structures. The method further includes updating the neural network based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values.