Multiple linear regression parameters for determining fatigue-based entropy characterisation of magnesium alloy

This paper presents the development of the multiple linear regression approach based on the stress ratio and applied load that was assessed using entropy generation. The energy dissipation is associated with material degradation to determine the fatigue life with consideration to the irreversible thermodynamic framework. This relationship was developed by predicting a complete entropy generation using a statistical approach, where a constant amplitude loading was applied to evaluate the fatigue life. By conducting compact tension tests, different stress ratios were applied to the specimen. During the tests, the temperature change was observed. The lowest entropy generation was 2.536 MJm-3 K-1 when 3,000N load with a stress ratio of 0.7 was applied to the specimen. The assumptions of the models were considered through graphical residual analysis. As a result, the predicted regression model based on the applied load and stress ratio was found to agree with the results of the experiment, with only 9.3% from the actual experiment. Therefore, the entropy generation can be predicted to access the dissipated energy as an irreversible degradation of a metallic material, subjected to cyclic elastic-plastic loading. Thermodynamic entropy is shown to play an important role in the fatigue process to trace the fatigue life.