Machine learning simulation of finite element analysis in augmented reality
Abstract
Media, method and system for approximating a finite element analysis texture map for an object. To accomplish this, the object is converted to a computer generated model and finite element analysis is performed for a plurality of different simulated inputs to generate a plurality of simulated mappings. Each simulated mapping is converted into a simulated texture map. A machine learning model is trained on the simulated inputs and simulated texture maps to generate a texture map which approximates a finite element analysis. The machine learning model receives a user input and generates the texture map therefrom. The texture map is then wrapped to the object and displayed.
- Inventors:
- Issue Date:
- Research Org.:
- Kansas City Plant (KCP), Kansas City, MO (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1987053
- Patent Number(s):
- 11557078
- Application Number:
- 17/667,258
- Assignee:
- Honeywell Federal Manufacturing & Technologies, LLC (Kansas City, MO)
- DOE Contract Number:
- NA0002839
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 02/08/2022
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Scherer, Derek Carl. Machine learning simulation of finite element analysis in augmented reality. United States: N. p., 2023.
Web.
Scherer, Derek Carl. Machine learning simulation of finite element analysis in augmented reality. United States.
Scherer, Derek Carl. Tue .
"Machine learning simulation of finite element analysis in augmented reality". United States. https://www.osti.gov/servlets/purl/1987053.
@article{osti_1987053,
title = {Machine learning simulation of finite element analysis in augmented reality},
author = {Scherer, Derek Carl},
abstractNote = {Media, method and system for approximating a finite element analysis texture map for an object. To accomplish this, the object is converted to a computer generated model and finite element analysis is performed for a plurality of different simulated inputs to generate a plurality of simulated mappings. Each simulated mapping is converted into a simulated texture map. A machine learning model is trained on the simulated inputs and simulated texture maps to generate a texture map which approximates a finite element analysis. The machine learning model receives a user input and generates the texture map therefrom. The texture map is then wrapped to the object and displayed.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2023},
month = {1}
}
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