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

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...

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Vydáno v:	Computation Ročník 7; číslo 1; s. 10
Hlavní autoři:	Ahmad, Hafiz Waqar, Hwang, Jeong Ho, Javed, Kamran, Chaudry, Umer Masood, Bae, Dong Ho
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 02.02.2019
Témata:	accelerated life testing Accelerated life tests Algorithms Alternative energy sources artificial neural network Artificial neural networks Bayesian analysis bayesian regularization algorithm Carbon dioxide Climate change Corrosion Dissimilar material joining dissimilar material weld Dissimilar materials Dissimilar metals Efficiency Environmental impact Failure modes Fatigue failure Fatigue life fatigue life prediction Fatigue tests Hybrid structures Industrial plant emissions Life prediction Load Metal fatigue Metallurgy Neural networks Nickel base alloys Power plants Regularization Renewable resources Solar energy Statistical analysis Steel alloys Superalloys Weibull distribution Welded joints Welded structures
ISSN:	2079-3197, 2079-3197
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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.
AbstractList	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. 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.
Author	Ahmad, Hafiz Waqar Bae, Dong Ho Javed, Kamran Hwang, Jeong Ho Chaudry, Umer Masood
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ContentType	Journal Article
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SubjectTerms	accelerated life testing Accelerated life tests Algorithms Alternative energy sources artificial neural network Artificial neural networks Bayesian analysis bayesian regularization algorithm Carbon dioxide Climate change Corrosion Dissimilar material joining dissimilar material weld Dissimilar materials Dissimilar metals Efficiency Environmental impact Failure modes Fatigue failure Fatigue life fatigue life prediction Fatigue tests Hybrid structures Industrial plant emissions Life prediction Load Metal fatigue Metallurgy Neural networks Nickel base alloys Power plants Regularization Renewable resources Solar energy Statistical analysis Steel alloys Superalloys Weibull distribution Welded joints Welded structures
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Title	Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach
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