{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T22:20:08Z","timestamp":1775254808891,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,2,2]],"date-time":"2019-02-02T00:00:00Z","timestamp":1549065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>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\u2013Marquardt (LM), were employed for training ANN. The Bayesian regularization training algorithm exhibited a better performance than the Levenberg\u2013Marquardt algorithm. The results confirmed that the assessment methods are effective for lifetime prediction of dissimilar material welded joints.<\/jats:p>","DOI":"10.3390\/computation7010010","type":"journal-article","created":{"date-parts":[[2019,2,5]],"date-time":"2019-02-05T11:31:07Z","timestamp":1549366267000},"page":"10","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2413-9555","authenticated-orcid":false,"given":"Hafiz Waqar","family":"Ahmad","sequence":"first","affiliation":[{"name":"School of mechanical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea"}]},{"given":"Jeong Ho","family":"Hwang","sequence":"additional","affiliation":[{"name":"School of mechanical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4277-9943","authenticated-orcid":false,"given":"Kamran","family":"Javed","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea"}]},{"given":"Umer Masood","family":"Chaudry","sequence":"additional","affiliation":[{"name":"School of Advanced Materials Science &amp; Engineering, 2066, Seobu-Ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea"}]},{"given":"Dong Ho","family":"Bae","sequence":"additional","affiliation":[{"name":"School of mechanical Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.5038\/2326-3652.6.2.4865","article-title":"Maximum efficiency of a wind turbine","volume":"6","author":"Blackwood","year":"2016","journal-title":"UJMM"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1002\/pip.3040","article-title":"Solar cell efficiency tables (version 52)","volume":"26","author":"Green","year":"2018","journal-title":"Prog. Photovolt. Res. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.geothermics.2013.11.001","article-title":"Efficiency of Geothermal Power Plants: A Worldwide Review","volume":"51","author":"Moon","year":"2014","journal-title":"Geothermics"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xie, X., Wu, Y., Chi, C., and Zhang, M. (2015). Superalloys for Advanced Ultra-Super-Critical Fossil Power Plant Application. Superalloys, 51\u201376.","DOI":"10.5772\/61139"},{"key":"ref_5","first-page":"554","article-title":"Microstructral, Mechanical and corrosion investigations of ship steel-Aluminium bimetal composites produced by explosive welding","volume":"8","author":"Yakup","year":"2018","journal-title":"Metals"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.marstruc.2017.10.004","article-title":"Full-field analysis of Al-Fe explosive welded joints for shipbuilding applications","volume":"57","author":"Corigliano","year":"2018","journal-title":"Mar. Struc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.oceaneng.2018.04.070","article-title":"Non linear finite element simulation of explosise welded joints of dissimilar metals for shipbuilding applications","volume":"160","author":"Pasqualino","year":"2018","journal-title":"Ocean Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1016\/j.matdes.2010.10.017","article-title":"Recent developments in explosive welding","volume":"32","author":"Fehim","year":"2011","journal-title":"Mat. Des."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1214\/088342306000000321","article-title":"A Review of Accelerated Test Models","volume":"21","author":"Escobar","year":"2006","journal-title":"Stat. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/TR.2012.2194190","article-title":"Overview of reliability testing","volume":"61","author":"Elsayed","year":"2012","journal-title":"IEEE Trans. Reliab."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cox, D.R. (1992). Regression Models and Life-Tables, Springer.","DOI":"10.1007\/978-1-4612-4380-9_37"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1080\/03610928508828940","article-title":"A general model for testing the proportional hazards and the accelerated failure time hypotheses in the analysis of censored survival data with covariates","volume":"14","author":"Ciampi","year":"1985","journal-title":"Commun. Stat. Theory Methods"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1080\/07408170500208362","article-title":"An extended linear hazard regression model with application to time-dependent dielectric breakdown of thermal oxides","volume":"38","author":"Elsayed","year":"2006","journal-title":"IIE Trans."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1080\/00207720500160084","article-title":"International Journal of Systems Science Modelling accelerated life testing based on mean residual life Modelling accelerated life testing based on mean residual life","volume":"36","author":"Zhao","year":"2005","journal-title":"Int. J. Syst. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1017\/S0071368600005164","article-title":"Mortality models and their uses in demography","volume":"33","author":"Brass","year":"1974","journal-title":"Trans. Fac. Actuar."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ahmad, H., Hwang, J., Lee, J., and Bae, D. (2016). An Assessment of the Mechanical Properties and Microstructural Analysis of Dissimilar Material Welded Joint between Alloy 617 and 12Cr Steel. Metals, 6.","DOI":"10.3390\/met6100242"},{"key":"ref_17","first-page":"305","article-title":"Determination of Residual Stresses in Structural Shapes","volume":"29","author":"Weldments","year":"1992","journal-title":"Weld. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0261-3069(89)80019-6","article-title":"Steel and Nickel Alloys at High Temperatures","volume":"10","author":"Meetham","year":"1989","journal-title":"Mater. Des."},{"key":"ref_19","unstructured":"(2018, May 18). Special Metals High\u2013Performance Nickel Alloys Leveraging a Network of Expertise. Available online: http:\/\/www.specialmetals.com\/assets\/smc\/documents\/pcc-8064-sm-alloy-handbook-v04.pdf."},{"key":"ref_20","unstructured":"ASTM International (2009). Standard Test Methods for Tension Testing of Metallic Materials, ASTM International."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ahmad, H.W., Lee, J.H., Hwang, J.H., and Bae, D.H. (2017). Welding Residual Stress Analysis and Fatigue Strength Assessment of Multi-Pass Dissimilar Material Welded Joint between Alloy 617 and 12Cr Steel. Metals, 8.","DOI":"10.3390\/met8010021"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/0013-7944(90)90262-F","article-title":"Fatigue processes in metals\u2014role of aqueous environments","volume":"36","author":"Sudarshan","year":"1990","journal-title":"Eng. Fract. Mech."},{"key":"ref_23","first-page":"124","article-title":"Influence of exposure to an aggressive environment on cyclic fatigue response and life of an alloy steel","volume":"14","author":"Gowda","year":"2017","journal-title":"J. Eng. Res."},{"key":"ref_24","first-page":"229","article-title":"Pre-crack fatigue damage and crack initiation under corrosion fatigue conditions","volume":"Volume 94","author":"Seidel","year":"1995","journal-title":"Proceedings of the HH Uhlig Memorial Symposium"},{"key":"ref_25","first-page":"1264","article-title":"Analysis of different activation functions using back propagation neural networks","volume":"47","author":"Sibi","year":"2013","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/S0304-4076(96)01818-0","article-title":"An R-squared measure of goodness of fit for some common nonlinear regression models","volume":"77","author":"Cameron","year":"1997","journal-title":"J. Econom."},{"key":"ref_27","unstructured":"Nielsen, M. (2019, February 02). Available online: http:\/\/neuralnetworksanddeeplearning.com\/."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kayri, M. (2016). Predictive Abilities of Bayesian Regularization and Levenberg\u2013Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data. Math. Comput. Appl., 21.","DOI":"10.3390\/mca21020020"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/7\/1\/10\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:30:43Z","timestamp":1760185843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/7\/1\/10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,2]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["computation7010010"],"URL":"https:\/\/doi.org\/10.3390\/computation7010010","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,2]]}}}