Fretting Fatigue Life Prediction for Aluminum Alloy Based on Particle-Swarm-Optimized Back Propagation Neural Network

Author:

Li Xin1ORCID,Yang Haoran1,Yang Jianwei1

Affiliation:

1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Abstract

Fretting fatigue is a specific fatigue phenomenon. Due to the complex mechanisms and multitude of influencing factors, it is still hard to predict fretting fatigue life accurately, despite there being many works on this topic. This paper developed a particle-swarm-optimized back propagation neural network to predict the fretting fatigue life of aluminum alloys using the test data gathered from the published literature. A commonly used critical plane model, the Smith, Watson, and Topper criterion, was used as a contrast. The analysis result shows that the proposed fretting fatigue life prediction neural network model achieves a higher prediction accuracy compared to the traditional SWT model. Experimental validation demonstrates the effectiveness of the model in improving the accuracy of fretting fatigue life prediction. This research provides a new data-driven methodology for fretting fatigue life prediction.

Funder

Science and Technology Plan Project of State Administration for Market Regulation of China Project

Beijing University of Civil Engineering and Architecture

Publisher

MDPI AG

Reference54 articles.

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3. Croccolo, D., De Agostinis, M., Fini, S., Olmi, G., Robusto, F., and Scapecchi, C. (2022). Fretting Fatigue in Mechanical Joints: A Literature Review. Lubricants, 10.

4. Beard, J. (1982). An Investigation into the Mechanisms of Fretting Fatigue. [Ph.D. Thesis, University of Salford]. Available online: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.237768.

5. Fretting Corrosion;Waterhouse;Proc. Inst. Mech. Eng.,1955

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