Fatigue Life Predictions of Metal Matrix CompositesUsing Artificial Neural Networks

Archives of Metallurgy and Materials

R. Kara¹  I. Uygur²  E. Toklu²  S. Saridemir³  A. Cicek⁴ 

[1] DUZCE UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING, 81620, DUZCE, TURKEY;DUZCE UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF MECHANICAL ENGINEERING, 81620, DUZCE, TURKEY;DUZCE UNIVERSITY, FACULTY OF TECHNOLOGY, DEPARTMENT OF MANUFACTURING ENGINEERING, DUZCE, TURKEY;YI LDIRIM BEYAZIT UNIVERSITY, FACULTY OF ENGINEERING AND NATURAL SCIENCES, DEPARTMENT OF MECHANICAL ENGINEERING, ANKARA, TURKEY

关键词: Keywords: MMCs;    Fatigue life prediction;    Artificial neural networks;   

DOI  :  10.2478/amm-2014-0016

学科分类：金属与冶金

来源: Akademia Gorniczo-Hutnicza im. Stanislawa Staszica / University of Mining and Metallurgy

【 摘 要 】

In this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geometries, and different temperatures have been performed by using artificial neural networks (ANN) approach. Input parameters of the model comprise various materials (M), such as particle size and volume fraction of reinforcement, stress concentration factor (Kt), R ratio (R), peak stress (S), temperatures (T), whereas, output of the ANN model consist of number of failure cycles. ANN controller was trained with Levenberg-Marquardt (LM) learning algorithm. The tested actual data and predicted data were simulated by a computer program developed on MATLAB platform. It is shown that the model provides intimate fatigue life estimations compared with actual tested data.

【 授权许可】

Unknown