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Performance evaluation of deep learning approaches for predicting mechanical fields in composites

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Abstract

This paper presents a rigorous and critical methodology for evaluating the performance of deep learning (DL) techniques in predicting mechanical responses within the microstructural representation of composites. In the past few years, deep learning has emerged as a powerful tool and an efficient surrogate for finite element analysis in computational mechanics. This research addresses questions regarding the suitability of common error metrics for evaluating the accuracy of DL techniques in predicting full-field mechanical responses of composites. Through comparative analysis, we evaluate the performance and identify the limitations of two DL frameworks in predicting the linear von Mises stress distribution within the microstructure of the selected fiber-reinforced composite. The first DL method is based on the residual network, while the second utilizes U-Net architecture. We use several evaluation metrics, including different types of error and statistical measures and examine their suitability. Additionally, this study also investigates the influence of the size of the training and validation dataset, ranging from 50 to 2000 samples, on the predictive performance of the employed ResNet and U-Net based approaches.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors gratefully acknowledge the support from the Air Force Office of Scientific Research (AFOSR) Young Investigator Program (YIP) award #FA9550-20-1-0281.

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Authors and Affiliations

  1. Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, 3775 Discovery Dr, Boulder, CO, 80309-0429, USA

    Marwa Yacouti & Maryam Shakiba

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  1. Marwa Yacouti
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  2. Maryam Shakiba
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Contributions

MY: Conceptualization, Methodology, Software, Writing—original draft. MS: Conceptualization, Writing review and editing, Supervision, Funding acquisition.

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Correspondence to Maryam Shakiba.

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Yacouti, M., Shakiba, M. Performance evaluation of deep learning approaches for predicting mechanical fields in composites. Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-01966-4

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