Application of Machine Learning in Predicting the Fatigue behaviour of Materials Using Deep Learning

Abstract

Accurate prediction of the fatigue behaviour of materials is crucial for ensuring the reliability and durability of structural components in various engineering applications. Machine learning (ML) techniques have demonstrated significant potential in predicting fatigue behaviour by analysing complex datasets. This research paper explores the application of deep learning, a subset of ML, for predicting the fatigue behaviour of materials. The study focuses on the development and optimization of deep learning models to accurately predict fatigue life and failure modes based on material properties, loading conditions, and other relevant factors. The research aims to improve the understanding and prediction of fatigue behaviour, leading to enhanced design and optimization of materials and structures.

The prediction of fatigue behaviour in materials is a critical aspect in engineering design and structural integrity assessment. Traditional approaches rely on empirical models and physical testing, which can be time-consuming and resource-intensive. In recent years, the application of machine learning, particularly deep learning techniques, has shown promising results in predicting the fatigue behaviour of materials. This paper presents an analysis of the application of machine learning, specifically deep learning, in predicting the fatigue behaviour of materials. The study focuses on the use of deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyse the complex relationships between material properties, loading conditions, and fatigue life. The paper discusses the methodology for training and validating the deep learning models using available fatigue data sets. Furthermore, it examines the performance and accuracy of the models in predicting fatigue life compared to traditional approaches. The findings suggest that deep learning models can effectively capture the nonlinear and intricate patterns in fatigue data, leading to accurate predictions of fatigue life. The practical implications of integrating machine learning into fatigue prediction are discussed, including the potential for accelerated design optimization, reduced testing requirements, and enhanced structural reliability. The contribution of this study lies in the exploration and evaluation of deep learning techniques for predicting the fatigue behaviour of materials, providing insights into the capabilities and limitations of machine learning approaches in this domain. Machine learning, particularly deep learning, as a valuable tool in predicting the fatigue behaviour of materials, enabling more efficient and reliable engineering design processes.