Welding Process Optimization With Artificial Neural Network Applications

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dc.contributor.authorAktepe, Adnan
dc.contributor.authorErsoz, Suleyman
dc.contributor.authorLuy, Murat
dc.date.accessioned2020-06-25T18:12:23Z
dc.date.available2020-06-25T18:12:23Z
dc.date.issued2014
dc.departmentKırıkkale Üniversitesi
dc.descriptionLUY, Murat/0000-0002-2378-0009; Aktepe, Adnan/0000-0002-3340-244X
dc.description.abstractCorrect detection of input and output parameters of a welding process is significant for successful development of an automated welding operation. In welding process literature, we observe that output parameters are predicted according to given input parameters. As a new approach to previous efforts, this paper presents a new modeling approach on prediction and classification of welding parameters. 3 different models are developed on a critical welding process based on Artificial Neural Networks (ANNs) which are (0 Output parameter prediction, (ii) Input parameter prediction (reverse application of output prediction model) and (iii) Classification of products. In this study, firstly we use Pareto Analysis for determining uncontrollable input parameters of the welding process based on expert views. With the help of these analysis, 9 uncontrollable parameters are determined among 22 potential parameters. Then, the welding process of ammunition is modeled as a multi-input multi-output process with 9 input and 3 output parameters. 1st model predicts the values of output parameters according to given input values. 2nd model predicts the values of correct input parameter combination for a defect-free weld operation and 3rd model is used to classify the products whether defected or defect-free. 3rd model is also used for validation of results obtained by 1st and 2nd models. A high level of performance is attained by all the methods tested in this study. In addition, the product is a strategic ammunition in the armed forces inventory which is manufactured in a limited number of countries in the world. Before application of this study, the welding process of the product could not be carried out in a systematic way. The process was conducted by trial-and-error approach by changing input parameter values at each operation. This caused a lot of costs. With the help of this study, best parameter combination is found, tested, validated with ANNs and operation costs are minimized by 30%.en_US
dc.description.sponsorshipRepublic of Turkey Ministry of Science, Industry and TechnologyMinistry of Science, Industry & Technology - Turkey [00748.STZ.2010-2]en_US
dc.description.sponsorshipThis study was financially supported by a grant from Republic of Turkey Ministry of Science, Industry and Technology with Grant No.: 00748.STZ.2010-2.en_US
dc.identifier.citationAktepe, A., Ersöz, S., & Lüy, M. (2014). Welding Process Optimization With Artificial Neural Network Applications. Neural Network World, 24, 655-670.en_US
dc.identifier.doi10.14311/NNW.2014.24.037
dc.identifier.endpage670en_US
dc.identifier.issn1210-0552
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-84920744590
dc.identifier.scopusqualityQ4
dc.identifier.startpage655en_US
dc.identifier.urihttps://doi.org/10.14311/NNW.2014.24.037
dc.identifier.urihttps://hdl.handle.net/20.500.12587/5891
dc.identifier.volume24en_US
dc.identifier.wosWOS:000348408100006
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcad Sciences Czech Republic, Inst Computer Scienceen_US
dc.relation.ispartofNeural Network World
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectwelding process controlen_US
dc.subjectweld operationen_US
dc.titleWelding Process Optimization With Artificial Neural Network Applicationsen_US
dc.typeArticle

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