Abstract
The present work investigates the relationship between fatigue crack growth rate (da/dN) and stress intensity factor range (∆K) using machine learning models with the experimental fatigue crack growth rate (FCGR) data of cryo-rolled Al 2014 alloy. Various machine learning techniques developed recently provide a flexible and adaptable approach to explain the complex mathematical relations especially, non-linear functions. In the present work, three machine algorithms such as extreme learning machine (ELM), back propagation neural networks (BPNN) and curve fitting model are implemented to analyse FCGR of Al alloys. After tuning of networks with varying hidden layers and number of neurons, the trained models found to fit well to the tested data. The three tested models are compared with each other over the training as well as testing phase. The mean square error for predicting the FCG of cryo-rolled Al 2014 alloy by BPNN, ELM and curve fitting methods are 1.89, 1.84 and 0.09 respectively. While the ELM models outperform the rest of models in terms of training time, curve fitting model showed best performance in terms of accuracy over testing data with least mean square error (MSE). In terms of local optimisation, back propagation neural networks excel the other two models.
Highlights
Failure of metals by fatigue was common even in the pre-historical, Bronze and Iron ages
The fatigue crack growth rate (FCGR) tests were performed on the cryo-rolled CR and the samples annealed after cryo-rolling following ASTM
The activation function of extreme learning machine (ELM) should satisfy a condition that it Fatigue crack growthdifferentiable tests were conducted onThe the cryo-rolled sampleactivation and on cryo-rolled should be an infinitely function
Summary
Failure of metals by fatigue was common even in the pre-historical, Bronze and Iron ages. In initial stages of fatigue analysis, the components were designed for safe-life approach known as stress-based approach. Fail-safe criteria, known as strain-based approach, were used. In this case, the materials were tested to find out the strain amplitude at which the material fails by testing at stress values close to the tensile strength which enables to design the system for fail-safe. The materials were tested to find out the strain amplitude at which the material fails by testing at stress values close to the tensile strength which enables to design the system for fail-safe Both these methods have the drawback of wastage of materials like discarding the healthy material in case of safe-life method and redundancy in case of fail-safe method. The theory of linear elastic fracture mechanics (LEFM) determined by stress
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