Open Access

ARTICLE

Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning

Xingzi Yang¹, Wei Gao², Xiaodu Wang¹, Xiaowei Zeng^1,*

1 Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, 78249, USA
2 Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA

* Corresponding Author: Xiaowei Zeng. Email:

Computer Modeling in Engineering & Sciences 2024, 140(2), 1299-1313. https://doi.org/10.32604/cmes.2024.049371

Received 05 January 2024; Accepted 08 April 2024; Issue published 20 May 2024

Abstract

The bioinspired nacre or bone structure represents a remarkable example of tough, strong, lightweight, and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials. The bioinspired structure consists of hard grains and soft material interfaces. While the material interface has a very low volume percentage, its property has the ability to determine the bulk material response. Machine learning technology nowadays is widely used in material science. A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite. This model was trained on a comprehensive dataset of material response and interface properties, allowing it to make accurate predictions. The results of this study demonstrate the efficiency and high accuracy of the machine learning model. The successful application of machine learning into the material property prediction process has the potential to greatly enhance both the efficiency and accuracy of the material design process.

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Keywords

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