Determination of residual stress by artificial neural network in Hsla-100 steel weldments

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dc.contributor.authorHeidari, Mohammad
dc.date.accessioned2024-07-12T20:49:44Z
dc.date.available2024-07-12T20:49:44Z
dc.date.issued2009en_US
dc.departmentFakülteler, İnsan ve Toplum Bilimleri Fakültesi, Matematik Bölümüen_US
dc.description.abstractThe residual stress fields near the weld bead in HSLA-100 steel weldments were examined in detail by means of neural network. Many different specimens that were subjected to different conditions were studied. At first, the residual stress by x- ray diffraction is calculated. Then a neural network is created. The input of this network is heat input, preheat, and yield strength, temperature of age and measurement direction. Residual stresses were determined without any calculating or experiment on the surface by using this network under any condition of problem. However, accurate predictions of residual stress could not be obtained without a large number of time and money by experimentally method. An application of the back-propagation neural net work using short term measuring data is presented in this paper. In this study experimental and numerical methods are combined to determination of the residual stress. Some advantage of this numerical method is saving in time and money. The Artificial Neural Network (ANN) is superior to existing experimental techniques. In this study, the neural networks have been employed as a general approximation tool for estimation of the residual stresses in welding of steel. For this purpose used of three functions such as newelm, newff and newcf and by using of MATLAB software, the network is created. The Levenberg-Marquardt algorithm is chosen to perform the training of the networks. A number of samples are analyzed with ANN for parameters of residual stress and the results are compared with experimental method. Back Propagation Neural network (BPN) is used to approximation of residual stress. Resultant low relative error value of the test indicates the usability of the BPN in this area. The results show that, estimation by newelm function is better than newff and newcf functions because it has less error than another function. Also specimens which are subjected to different welding heat input have similar distributions of residual stress on the surface, but the magnitudes of stresses are different. Higher welding heat input generates smaller stress.en_US
dc.identifier.citationHeidari, M. (2009). Determination of residual stress by artificial neural network in Hsla-100 steel weldments. Maltepe Üniversitesi. s. 274.en_US
dc.identifier.endpage275en_US
dc.identifier.isbn9.78605E+12
dc.identifier.startpage274en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12415/2221
dc.institutionauthorHeidari, Mohammad
dc.language.isoenen_US
dc.publisherMaltepe Üniversitesien_US
dc.relation.ispartofInternational Conference of Mathematical Sciencesen_US
dc.relation.publicationcategoryUluslararası Konferans Öğesi - Başka Kurum Yazarıen_US
dc.rightsCC0 1.0 Universal*
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.snmzKY07543
dc.titleDetermination of residual stress by artificial neural network in Hsla-100 steel weldmentsen_US
dc.typeConference Object
dspace.entity.typePublication

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