20230196629. GENERATING MICROSTRUCTURES FOR MATERIALS BASED ON MACHINE LEARNING MODELS simplified abstract (VOLKSWAGEN AKTIENGESELLSCHAFT)
GENERATING MICROSTRUCTURES FOR MATERIALS BASED ON MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Wesley Teskey of Foster City CA (US)
GENERATING MICROSTRUCTURES FOR MATERIALS BASED ON MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230196629 titled 'GENERATING MICROSTRUCTURES FOR MATERIALS BASED ON MACHINE LEARNING MODELS
Simplified Explanation
The patent application describes a method for generating images of the microstructure of a material using a generative adversarial network (GAN) and a Gaussian mixture model (GMM). The method involves determining the sizes and locations of a set of spheres within the material volume based on the GMM. The GAN is then used to generate a set of images depicting the microstructure of the material.
- Method for generating images of the microstructure of a material using GAN and GMM
- Determines sizes of spheres within the material volume based on GMM
- Determines locations of spheres within the material volume
- Generates a set of images depicting the microstructure using GAN
Potential Applications
- Material science research
- Quality control in manufacturing processes
- Non-destructive testing of materials
Problems Solved
- Lack of efficient methods for visualizing the microstructure of materials
- Difficulty in accurately determining the sizes and locations of spheres within a material volume
Benefits
- Provides a method for generating realistic images of material microstructure
- Enables better understanding and analysis of material properties
- Facilitates improved quality control and testing processes
Original Abstract Submitted
in one embodiment, a method is provided. the method includes determining a set of spheres for a volume of a material. the volume of the material comprises the set of spheres and additional materials. the sizes of the set of spheres are based on a gaussian mixture model (gmm). the method also includes determining a set of locations for the set of spheres within the volume of the material. the method further includes generating a set of images of the volume of the material based on a first generative adversarial network and a second generative adversarial network. the set of images depict a microstructure of the volume of material.
