StressNet - Deep learning to predict stress with fracture propagation in brittle materials

Wang, Yinan; Oyen, Diane; Guo, Weihong; Mehta, Anishi; Scott, Cory Braker; Panda, Nishant; Fernández-Godino, M. Giselle; Srinivasan, Gowri; Yue, Xiaowei

doi:10.1038/s41529-021-00151-y

Title: StressNet - Deep learning to predict stress with fracture propagation in brittle materials

Abstract

Abstract Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds,more »« less

Authors:

; Oyen, Diane;

; Mehta, Anishi;

; Panda, Nishant; Fernández-Godino, M. Giselle; Srinivasan, Gowri;

Publication Date:: Wed Feb 10 00:00:00 EST 2021

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE Laboratory Directed Research and Development (LDRD) Program

OSTI Identifier:: 1765066

Alternate Identifier(s):: OSTI ID: 1822735

Report Number(s):: LA-UR-19-31067
Journal ID: ISSN 2397-2106; 6; PII: 151

Grant/Contract Number:: 89233218CNA000001

Resource Type:: Published Article

Journal Name:: npj Materials Degradation

Additional Journal Information:: Journal Name: npj Materials Degradation Journal Volume: 5 Journal Issue: 1; Journal ID: ISSN 2397-2106

Publisher:: Nature Publishing Group

Country of Publication:: United Kingdom

Language:: English

Subject:: 36 MATERIALS SCIENCE; Computational methods; mechanical engineering

Citation Formats


                    Wang, Yinan, Oyen, Diane, Guo, Weihong, Mehta, Anishi, Scott, Cory Braker, Panda, Nishant, Fernández-Godino, M. Giselle, Srinivasan, Gowri, and Yue, Xiaowei. StressNet - Deep learning to predict stress with fracture propagation in brittle materials.  United Kingdom: N. p., 2021. 
Web.  doi:10.1038/s41529-021-00151-y.

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                    Wang, Yinan, Oyen, Diane, Guo, Weihong, Mehta, Anishi, Scott, Cory Braker, Panda, Nishant, Fernández-Godino, M. Giselle, Srinivasan, Gowri, & Yue, Xiaowei. StressNet - Deep learning to predict stress with fracture propagation in brittle materials.  United Kingdom.  https://doi.org/10.1038/s41529-021-00151-y

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                    Wang, Yinan, Oyen, Diane, Guo, Weihong, Mehta, Anishi, Scott, Cory Braker, Panda, Nishant, Fernández-Godino, M. Giselle, Srinivasan, Gowri, and Yue, Xiaowei. Wed .  
"StressNet - Deep learning to predict stress with fracture propagation in brittle materials".  United Kingdom.  https://doi.org/10.1038/s41529-021-00151-y.

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@article{osti_1765066,

  title        = {StressNet - Deep learning to predict stress with fracture propagation in brittle materials},

  author       = {Wang, Yinan and Oyen, Diane and Guo, Weihong and Mehta, Anishi and Scott, Cory Braker and Panda, Nishant and Fernández-Godino, M. Giselle and Srinivasan, Gowri and Yue, Xiaowei},

  abstractNote = {Abstract Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.},

  doi          = {10.1038/s41529-021-00151-y},

  journal      = {npj Materials Degradation},

  number       = 1,

  volume       = 5,

  place        = {United Kingdom},

  year         = {Wed Feb 10 00:00:00 EST 2021},

  month        = {Wed Feb 10 00:00:00 EST 2021}

}

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Title: StressNet - Deep learning to predict stress with fracture propagation in brittle materials

Abstract

Citation Formats

Bidirectional recurrent neural networks journal, January 1997

Feasibility analysis of composite fuselage shape control via finite element analysis journal, January 2018

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A review of deep learning in the study of materials degradation journal, November 2018

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Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials journal, July 2018

A generalized anisotropic deformation formulation for geomaterials journal, October 2015

Accelerating high-strain continuum-scale brittle fracture simulations with machine learning journal, January 2021

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition journal, April 2018

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Superiorities of support vector machine in fracture prediction and gassiness evaluation journal, October 2008

Finite element analysis of residual stresses and thin plate distortion after face milling conference, January 2015

Box-Jenkins Seasonal Forecasting: Problems in a Case-Study journal, January 1973

Strain rate concentration and dynamic stress concentration for double-edge-notched specimens subjected to high-speed tensile loads: STRAIN RATE CONCENTRATION AND DYNAMIC STRESS CONCENTRATION journal, July 2014

Brittle-material failures as indicators of stress-concentration factors: Test program leads to development of method for determining stress-concentration factors in components of complicated shapes. Plaster-silica mortar is used for test models journal, March 1962

Long Short-Term Memory journal, November 1997

Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks journal, May 2019

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks journal, September 2019

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Bidirectional recurrent neural networks
journal, January 1997

Feasibility analysis of composite fuselage shape control via finite element analysis
journal, January 2018

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journal, June 2018

A review of deep learning in the study of materials degradation
journal, November 2018

Virtual assembly and residual stress analysis for the composite fuselage assembly process
journal, July 2019

Tensor decomposition to compress convolutional layers in deep learning
journal, April 2021

On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
conference, January 2014

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journal, April 2018

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journal, February 2019

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journal, July 2018

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journal, January 2021

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journal, April 2018

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journal, February 2018

Superiorities of support vector machine in fracture prediction and gassiness evaluation
journal, October 2008

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conference, January 2015

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journal, January 1973

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journal, July 2014

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