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Computation 2019, 7(1), 10; https://doi.org/10.3390/computation7010010

Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach

1
School of mechanical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea
2
College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea
3
School of Advanced Materials Science & Engineering, 2066, Seobu-Ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea
*
Author to whom correspondence should be addressed.
Received: 27 December 2018 / Revised: 11 January 2019 / Accepted: 14 January 2019 / Published: 2 February 2019
(This article belongs to the Section Computational Engineering)
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Abstract

Welding alloy 617 with other metals and alloys has been receiving significant attention in the last few years. It is considered to be the benchmark for the development of economical hybrid structures to be used in different engineering applications. The differences in the physical and metallurgical properties of dissimilar materials to be welded usually result in weaker structures. Fatigue failure is one of the most common failure modes of dissimilar material welded structures. In this study, fatigue life prediction of dissimilar material weld was evaluated by the accelerated life method and artificial neural network approach (ANN). The accelerated life testing approach was evaluated for different distributions. Weibull distribution was the most appropriate distribution that fits the fatigue data very well. Acceleration of fatigue life test data was attained with 95% reliability for Weibull distribution. The probability plot verified that accelerating variables at each level were appropriate. Experimental test data and predicted fatigue life were in good agreement with each other. Two training algorithms, Bayesian regularization (BR) and Levenberg–Marquardt (LM), were employed for training ANN. The Bayesian regularization training algorithm exhibited a better performance than the Levenberg–Marquardt algorithm. The results confirmed that the assessment methods are effective for lifetime prediction of dissimilar material welded joints. View Full-Text
Keywords: fatigue life prediction; accelerated life testing; Weibull distribution; artificial neural network; bayesian regularization algorithm; dissimilar material weld fatigue life prediction; accelerated life testing; Weibull distribution; artificial neural network; bayesian regularization algorithm; dissimilar material weld
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ahmad, H.W.; Hwang, J.H.; Javed, K.; Chaudry, U.M.; Bae, D.H. Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach. Computation 2019, 7, 10.

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