EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA

Anindya Bhaduri; Nesar Ramachandra; Sandipp Krishnan Ravi; Lele Luan; Piyush Pandita; Prasanna Balaprakash; Mihai Anitescu; Changjie Sun; Liping Wang

doi:10.1115/DETC2023115077

EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA

Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang

CCSD Planning, Partnerships, Operations

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximizes performance. In such scenarios, surrogate models are employed to make the forward mapping efficient. Still, the high dimensionality of the input microstructure and the output field of interest may render them inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have been typically found to be very useful in handling such high-dimensional problems. In this paper, the system under study is a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic FEM model. We consider two deep CNN architectures, a modified Convolutional Autoencoder (CAE) framework with a fully connected bottleneck and a UNet Convolutional Neural Network (CNN), and compare their accuracy in predicting the Von Mises stress field for any given input void shape in the FEM model. A sensitivity analysis is also performed using the two methods, where the variation in the prediction accuracy on unseen test data is studied with the increasing number of training samples from 20 to 100.

Original language	English
Title of host publication	43rd Computers and Information in Engineering Conference (CIE)
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791887295
DOIs	https://doi.org/10.1115/DETC2023115077
State	Published - 2023
Event	ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023 - Boston, United States Duration: Aug 20 2023 → Aug 23 2023

Publication series

Name	Proceedings of the ASME Design Engineering Technical Conference
Volume	2

Conference

Conference	ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Country/Territory	United States
City	Boston
Period	08/20/23 → 08/23/23

Funding

This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office, Award Number DE-AC0206H11357. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Work at Argonne National Laboratory was supported by the U.S. Department of Energy, Office of High Energy Physics. Argonne, a U.S. Department of Energy Office of Science Laboratory, is operated by UChicago Argonne LLC under contract no. DEAC02-06CH11357. This manuscript has been authored by UTBattelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan). Part of the analysis here is carried out on Swing, a GPU system at the Laboratory Computing Resource Center (LCRC) of Argonne National Laboratory. We would also like to thank Dr. Aymeric Moinet, at General Electric Research, for his insights into the ellipsoidal void problem. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office, Award Number DE-AC0206H11357. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Work at Argonne National Laboratory was supported by the U.S. Department of Energy, Office of High Energy Physics. Argonne, a U.S. Department of Energy Office of Science Laboratory, is operated by UChicago Argonne LLC under contract no. DE-AC02-06CH11357. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan). Part of the analysis here is carried out on Swing, a GPU system at the Laboratory Computing Resource Center (LCRC) of Argonne National Laboratory. We would also like to thank Dr. Aymeric Moinet, at General Electric Research, for his insights into the ellipsoidal void problem.

Keywords

convolutional neural networks
deep learning
sparse data
stress field
surrogate modeling
void geometry

Access to Document

10.1115/DETC2023115077

Cite this

Bhaduri, A., Ramachandra, N., Ravi, S. K., Luan, L., Pandita, P., Balaprakash, P., Anitescu, M., Sun, C., & Wang, L. (2023). EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA. In 43rd Computers and Information in Engineering Conference (CIE) Article v002t02a004 (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2023115077

Bhaduri, Anindya ; Ramachandra, Nesar ; Ravi, Sandipp Krishnan et al. / EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA. 43rd Computers and Information in Engineering Conference (CIE). American Society of Mechanical Engineers (ASME), 2023. (Proceedings of the ASME Design Engineering Technical Conference).

@inproceedings{0391b68d5fd94ec0a71d95a3ade42264,

title = "EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA",

abstract = "Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximizes performance. In such scenarios, surrogate models are employed to make the forward mapping efficient. Still, the high dimensionality of the input microstructure and the output field of interest may render them inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have been typically found to be very useful in handling such high-dimensional problems. In this paper, the system under study is a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic FEM model. We consider two deep CNN architectures, a modified Convolutional Autoencoder (CAE) framework with a fully connected bottleneck and a UNet Convolutional Neural Network (CNN), and compare their accuracy in predicting the Von Mises stress field for any given input void shape in the FEM model. A sensitivity analysis is also performed using the two methods, where the variation in the prediction accuracy on unseen test data is studied with the increasing number of training samples from 20 to 100.",

keywords = "convolutional neural networks, deep learning, sparse data, stress field, surrogate modeling, void geometry",

author = "Anindya Bhaduri and Nesar Ramachandra and Ravi, {Sandipp Krishnan} and Lele Luan and Piyush Pandita and Prasanna Balaprakash and Mihai Anitescu and Changjie Sun and Liping Wang",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 by The United States Government.; ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023 ; Conference date: 20-08-2023 Through 23-08-2023",

year = "2023",

doi = "10.1115/DETC2023115077",

language = "English",

series = "Proceedings of the ASME Design Engineering Technical Conference",

publisher = "American Society of Mechanical Engineers (ASME)",

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Bhaduri, A, Ramachandra, N, Ravi, SK, Luan, L, Pandita, P, Balaprakash, P, Anitescu, M, Sun, C & Wang, L 2023, EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA. in 43rd Computers and Information in Engineering Conference (CIE)., v002t02a004, Proceedings of the ASME Design Engineering Technical Conference, vol. 2, American Society of Mechanical Engineers (ASME), ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023, Boston, United States, 08/20/23. https://doi.org/10.1115/DETC2023115077

EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA. / Bhaduri, Anindya; Ramachandra, Nesar; Ravi, Sandipp Krishnan et al.
43rd Computers and Information in Engineering Conference (CIE). American Society of Mechanical Engineers (ASME), 2023. v002t02a004 (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Bhaduri, Anindya

AU - Ramachandra, Nesar

AU - Ravi, Sandipp Krishnan

AU - Luan, Lele

AU - Pandita, Piyush

AU - Balaprakash, Prasanna

AU - Anitescu, Mihai

AU - Sun, Changjie

AU - Wang, Liping

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N2 - Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximizes performance. In such scenarios, surrogate models are employed to make the forward mapping efficient. Still, the high dimensionality of the input microstructure and the output field of interest may render them inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have been typically found to be very useful in handling such high-dimensional problems. In this paper, the system under study is a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic FEM model. We consider two deep CNN architectures, a modified Convolutional Autoencoder (CAE) framework with a fully connected bottleneck and a UNet Convolutional Neural Network (CNN), and compare their accuracy in predicting the Von Mises stress field for any given input void shape in the FEM model. A sensitivity analysis is also performed using the two methods, where the variation in the prediction accuracy on unseen test data is studied with the increasing number of training samples from 20 to 100.

AB - Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximizes performance. In such scenarios, surrogate models are employed to make the forward mapping efficient. Still, the high dimensionality of the input microstructure and the output field of interest may render them inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have been typically found to be very useful in handling such high-dimensional problems. In this paper, the system under study is a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic FEM model. We consider two deep CNN architectures, a modified Convolutional Autoencoder (CAE) framework with a fully connected bottleneck and a UNet Convolutional Neural Network (CNN), and compare their accuracy in predicting the Von Mises stress field for any given input void shape in the FEM model. A sensitivity analysis is also performed using the two methods, where the variation in the prediction accuracy on unseen test data is studied with the increasing number of training samples from 20 to 100.

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T3 - Proceedings of the ASME Design Engineering Technical Conference

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Bhaduri A, Ramachandra N, Ravi SK, Luan L, Pandita P, Balaprakash P et al. EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA. In 43rd Computers and Information in Engineering Conference (CIE). American Society of Mechanical Engineers (ASME). 2023. v002t02a004. (Proceedings of the ASME Design Engineering Technical Conference). doi: 10.1115/DETC2023115077

EFFICIENT MAPPING BETWEEN VOID SHAPES AND STRESS FIELDS USING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH SPARSE DATA

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