Techniques for training a machine learning model to modify portions of shapes when generating designs for three-dimensional objects

a machine learning model and shape technology, applied in the field of computer science and computer-aided design, can solve the problems of solving each of the topology optimization problems, computational complexity, and the inability of generative design applications to produce numerous designs that are more convergent with the design objectives than the designs, and achieve the effect of reducing the computational complexity associated with solving the topology optimization problem and the overall computational complexity

Active Publication Date: 2022-04-28
AUTODESK INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The present invention provides methods for improving how computer-generated three dimensional (3D) models look like their original appearance during simulation testing by modifying certain parts of them according to specific structures from higher quality images captured at different levels of detail. This reduces the time required to generate new versions of these simulations compared to previous methodologies where each part was created separately beforehand. Overall this technology allows us to make better choices about which ones will have been generated afterward without having to wait too long until they become available again.

Problems solved by technology

This patented describes how we can efficiently generate solutions called topologies for three dimensional objects while minimizing their own weight. These technical means involve generating specific configurations around certain boundaries and specifying properties like materials used, manufacture restrictions, loading requirements, etc., along these edges. Then, applying optimal algorithmic calculations to optimize those configuration variables overall.

Method used

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  • Techniques for training a machine learning model to modify portions of shapes when generating designs for three-dimensional objects
  • Techniques for training a machine learning model to modify portions of shapes when generating designs for three-dimensional objects
  • Techniques for training a machine learning model to modify portions of shapes when generating designs for three-dimensional objects

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Embodiment Construction

[0001]The various embodiments relate generally to computer science and computer-aided design and, more specifically, to techniques for training a machine learning model to modify portions of shapes when generating designs for three-dimensional objects.

Description of the Related Art

[0002]Generative design for three-dimensional (“3D”) objects is a computer-aided design process that automatically generates designs for 3D objects that satisfy any number and type of design objectives and design constraints specified by a user. In some implementations, a generative design application specifies any number of topology optimization problems based on the design objectives and design constraints. Each problem specification includes a shape boundary and values for any number of parameters associated with the topology optimization problem. Some examples of parameters include, without limitation, material types, manufacturing methods, manufacturing constraints, load use cases, design constraints, de

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Abstract

In various embodiments, a training application trains a machine learning model to modify portions of shapes when designing 3D objects. The training application converts first structural analysis data having a first resolution to first coarse structural analysis data having a second resolution that is lower than the first resolution. Subsequently, the training application generates one or more training sets based on a first shape, the first coarse structural analysis data, and a second shape that is derived from the first shape. Each training set is associated with a different portion of the first shape. The training application then performs one or more machine learning operations on the machine learning model using the training set(s) to generate a trained machine learning model. The trained machine learning model modifies at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.

Description

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Claims

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Application Information

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Owner AUTODESK INC
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