Machine learning techniques for generating designs for three-dimensional objects

Patent metadata

Field	Value
Publication number	US-11468634-B2
Application number	US-202017083147-A
Country	US
Kind code	B2
Filing date	Oct 28, 2020
Priority date	Oct 28, 2020
Publication date	Oct 11, 2022
Grant date	Oct 11, 2022

Abstract

In various embodiments, a topology optimization application solves a topology optimization problem associated with designing a three-dimensional (“3D”) object. The topology optimization application coverts a first shape having a first resolution and representing the 3D object to a coarse shape having a second resolution that is lower than the first resolution. Subsequently, the topology optimization application computes coarse structural analysis data based on the coarse shape. The topology optimization application then uses a trained machine learning model to generate a second shape having the first resolution and representing the 3D object based on the first shape and the coarse structural analysis data. The trained machine learning model modifies a portion of a shape having the first resolution based on structural analysis data having the second resolution. Advantageously, generating the second shape based on structural analysis data having a lower resolution reduces computational complexity relative to prior art techniques.

First claim

What is claimed is:

1. A computer-implemented method for solving a topology optimization problem when designing a three-dimensional (“3D”) object, the method comprising:
converting a first shape having a first resolution and representing the 3D object to a coarse shape having a second resolution that is lower than the first resolution;
computing coarse structural analysis data based on the coarse shape; and
generating, via a trained machine learning model, a second shape having the first resolution and representing the 3D object based on the first shape and the coarse structural analysis data, wherein the trained machine learning model modifies a portion of a given shape having the first resolution based on structural analysis data having the second resolution.

2. The computer-implemented method of claim 1 , wherein the second shape is more convergent with a design objective associated with the topology optimization problem than the first shape.

3. The computer-implemented method of claim 1 , wherein converting the first shape to the coarse shape comprises performing one or more downsampling operations on the first shape.

4. The computer-implemented method of claim 1 , wherein the first shape comprises a 3D mesh of shape elements, and wherein each shape element is associated with a different voxel of a 3D grid having the first resolution.

5. The computer-implemented method of claim 4 , wherein each shape element includes at least one of a signed distance field or a keep-in label.

6. The computer-implemented method of claim 1 , wherein the coarse structural analysis data comprises at least one of a plurality of strain energy values, a plurality of displacements, or a plurality of rotations.

7. The computer-implemented method of claim 1 , wherein computing the coarse structural analysis data comprises:
performing one or more structural analysis operations on the coarse shape to generate non-normalized coarse structural analysis data; and
performing one or more normalization operations on at least a portion of the non-normalized coarse structural analysis data to generate the coarse structural analysis data.

8. The computer-implemented method of claim 1 , wherein generating the second shape comprises:
performing one or more partitioning operations on the coarse structural analysis data and on at least one of the first shape or the coarse shape to generate a plurality of inference sets;
inputting the inference sets into the trained machine learning model that, in response, outputs a plurality of modified portions of the first shape; and
aggregating the plurality of modified portions of the first shape to generate the second shape.

9. The computer-implemented method of claim 1 , wherein the trained machine learning model comprises a trained neural network.

10. The computer-implemented method of claim 1 , wherein the 3D object is designed via a generative design process that involves the topology optimization problem, and the first shape is generated based on a specification for the topology optimization problem.

11. One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to solve a topology optimization problem when designing a three-dimensional (“3D”) object by performing the steps of:
converting a first shape having a first resolution and representing the 3D object to a coarse shape having a second resolution that is lower than the first resolution;
computing coarse structural analysis data based on the coarse shape; and
generating, via a trained machine learning model, a second shape having the first resolution and representing the 3D object based on the first shape and the coarse structural analysis data, wherein the trained machine learning model modifies a portion of a given shape having the first resolution based on structural analysis data having the second resolution.

12. The one or more non-transitory computer readable media of claim 11 , wherein the second shape is more convergent with a design objective associated with the topology optimization problem than the first shape.

13. The one or more non-transitory computer readable media of claim 11 , wherein converting the first shape to the coarse shape comprising performing one or more field transfer operations or remeshing operations on the first shape.

14. The one or more non-transitory computer readable media of claim 11 , wherein the coarse shape comprises a 3D mesh of shape elements, and wherein each shape element is associated with a different voxel of a 3D grid having the second resolution.

15. The one or more non-transitory computer readable media of claim 11 , wherein the coarse structural analysis data comprises at least one of a plurality of strain energy values, a plurality of displacements, or a plurality of rotations.

16. The one or more non-transitory computer readable media of claim 11 , wherein computing the coarse structural analysis data comprises:
performing one or more structural analysis operations on the coarse shape to generate a plurality of strain energy values; and
performing one or more normalization operations on the plurality of strain energy values to generate the coarse structural analysis data.

17. The one or more non-transitory computer readable media of claim 11 , wherein generating the second shape comprises:
performing one or more partitioning operations on the coarse structural analysis data and on at least one of the first shape or the coarse shape to generate a plurality of inference sets;
inputting the plurality of inference sets into the trained machine learning model that, in response, outputs a plurality of modified portions of the first shape; and
aggregating the plurality of modified portions of the first shape to generate the second shape.

18. The one or more non-transitory computer readable media of claim 11 , wherein generating the second shape comprises:
computing a first inference set associated with a first portion of the first shape based on the coarse structural analysis data and at least one of the first shape or the coarse shape;
inputting the first inference set into the trained machine learning model that, in response, generates a modified first portion of the first shape; and
aggregating the modified first portion of the first shape with at least a modified second portion of the first shape to generate the second shape.

19. The one or more non-transitory computer readable media of claim 11 , wherein the 3D object is designed via a generative design process that involves the topology optimization problem, and the first shape is generated based on a specification for the topology optimization problem.

20. A system comprising:
one or more memories storing instructions; and
one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of:
converting a first shape having a first resolution and representing a three-dimensional (“3D”) object to a coarse shape having a second resolution that is lower than the first resolution;
computing coarse structural analysis data based on the coarse shape; and
generating, via a trained machine learning model, a second shape having the first resolution and representing the 3D object based on the first shape and the coarse structural analysis data, wherein the trained machine learning model modifies a portion of a given shape having the first resolution based on structural analysis data having the second

Assignees

• Autodesk Inc

Inventors

• Zhang Ran
• Fabian Morgan
• Ndip-Agbor Ebot
• Taylor Lee Morris

Classifications

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G06T17/20 Primary

  Finite element generation, e.g. wire-frame surface description, {tesselation} · CPC title