Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach
| العنوان: | Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach |
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| المؤلفون: | Kamran Javed, Hafiz Waqar Ahmad, Jeong Ho Hwang, Dong Ho Bae, Umer Masood Chaudry |
| المصدر: | Computation Volume 7 Issue 1 Computation, Vol 7, Iss 1, p 10 (2019) |
| بيانات النشر: | Multidisciplinary Digital Publishing Institute, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Probability plot, General Computer Science, Computer science, 0211 other engineering and technologies, 02 engineering and technology, Welding, 01 natural sciences, lcsh:QA75.5-76.95, dissimilar material weld, Theoretical Computer Science, law.invention, law, 0103 physical sciences, Bayesian regularization algorithm, Reliability (statistics), Weibull distribution, 010302 applied physics, 021103 operations research, Artificial neural network, business.industry, Applied Mathematics, Probabilistic logic, Structural engineering, Accelerated life testing, fatigue life prediction, Modeling and Simulation, accelerated life testing, lcsh:Electronic computers. Computer science, business, artificial neural network, Test data |
| الوصف: | 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&ndash Marquardt (LM), were employed for training ANN. The Bayesian regularization training algorithm exhibited a better performance than the Levenberg&ndash Marquardt algorithm. The results confirmed that the assessment methods are effective for lifetime prediction of dissimilar material welded joints. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2079-3197 |
| DOI: | 10.3390/computation7010010 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c60c27a343d4026977fe04edb771a539 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....c60c27a343d4026977fe04edb771a539 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20793197 |
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| DOI: | 10.3390/computation7010010 |