Generating microstructures for materials based on machine learning models
AssigneeVolkswagen Aktiengesellschaft
InventorWesley Teskey
Life of this patent
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Abstract
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.
Claims20
A method, comprising:
- determining a set of spheres for a volume of a material, wherein:
- the volume of the material comprises the set of spheres and additional materials;
- and
- sizes of the set of spheres are based on a Gaussian mixture model (GMM);
- determining a set of locations for the set of spheres within the volume of the material;
- generating a set of images of the volume of the material based on a first generative adversarial network and a second generative adversarial network, wherein the set of images depict a microstructure of the volume of material and wherein a first subset of set of images is generated by the first generative adversarial network and a second subset of the set of images is generated by the second adversarial network using the first subset as an input;
- and
- determining, by a machine learning model, one or more material properties of the volume of the material, wherein the set of images are provided as an input to the machine learning model.
An apparatus, comprising:
- a memory configured to store data;
- and
- a processing device coupled to the memory, the processing device configured to:
- determine a set of spheres for a volume of a material, wherein:
- the volume of the material comprises the set of spheres and additional materials;
- and
- sizes of the set of spheres are based on a Gaussian mixture model (GMM);
- determine a set of locations for the set of spheres within the volume of the material;
- generate a set of images of the volume of the material based on a first generative adversarial network and a second generative adversarial network, wherein the set of images depict a microstructure of the volume of material and wherein a first subset of set of images is generated by the first generative adversarial network and a second subset of the set of images is generated by the second adversarial network using the first subset as an input;
- and
- determine, based on a machine learning model, one or more material properties of the volume of the material, wherein the set of images are provided as an input to the machine learning model.
A non-transitory computer-readable storage medium including instructions that, when executed by a processing device, cause the processing device to perform operations comprising:
- determining a set of spheres for a volume of a material, wherein:
- the volume of the material comprises the set of spheres and additional materials;
- and
- sizes of the set of spheres are based on a Gaussian mixture model (GMM);
- determining a set of locations for the set of spheres within the volume of the material;
- generating a set of images of the volume of the material based on a first generative adversarial network and a second generative adversarial network, wherein the set of images depict a microstructure of the volume of material and wherein a first subset of set of images is generated by the first generative adversarial network and a second subset of the set of images is generated by the second adversarial network using the first subset as an input;
- and
- determining, based on a machine learning model, one or more material properties of the volume of the material, wherein the set of images are provided as an input to the machine learning model.
Dependencies are inferred from the claim text.