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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Vydané v:Computation Ročník 7; číslo 1; s. 10
Hlavní autori: Ahmad, Hafiz Waqar, Hwang, Jeong Ho, Javed, Kamran, Chaudry, Umer Masood, Bae, Dong Ho
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 02.02.2019
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2079-3197
2079-3197
DOI:10.3390/computation7010010