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.
Highlights
Climate change is one of the most difficult challenges facing the world today
The fatigue test specimens were extracted from dissimilar material welded plate as shown in
The advantage of a Bayesian regularization artificial neural network is its ability to of reveal potentially complex between inputs
Summary
Climate change is one of the most difficult challenges facing the world today. To prevent climate change, profound changes in the production, distribution, and consumption of energy are required.The increased emission of carbon dioxide due to various human activities is directly responsible for the increase in the Earth’s average temperature. Climate change is one of the most difficult challenges facing the world today. Profound changes in the production, distribution, and consumption of energy are required. The increased emission of carbon dioxide due to various human activities is directly responsible for the increase in the Earth’s average temperature. To reduce carbon dioxide (CO2 ) emissions and avoid the related environmental problems, scientists and engineers are always looking for methods with which the emission of exhaust gases can be mitigated. The use of renewable energy has become increasingly important in meeting future energy demands and limiting the exposure of CO2 , such as solar power plants, wind mills, and geothermal. The key issue associated with greener plants is the amount of energy being extracted from renewable sources of energy, i.e., the energy efficiency of renewable
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