A new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning models

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dc.authoridGupta, Munish/0000-0002-0777-1559
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorMashinini, Peter Madindwa
dc.contributor.authorShibi, C. Sherin
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorKrolczyk, Grzegorz M.
dc.contributor.authorSharma, Vishal S.
dc.date.accessioned2024-09-29T15:57:56Z
dc.date.available2024-09-29T15:57:56Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractDue to the manufacturing sector ' s digitalization and ability to combine quality measurement and production data, machine learning and deep learning for quality assurance hold enormous potential. In this situation, industries may process data to inform data-driven estimates of product quality, thanks to predictive excellence. This research investigates the machinability of Laser Powder Bed Fusion (LPBF) - 316L stainless steel specimens, focusing on the impact of cutting parameters and cooling conditions (Dry, MQL, CO 2 and CO 2 + MQL) on surface roughness. The research employs advanced data augmentation techniques, incorporating TransGAN and multihead attention (MHA) based Alexnet model for surface imperfection classification. The results highlight the effectiveness of the proposed methodology in accurately classifying surface conditions and underscore the superior performance of the MHA-Alexnet algorithm compared to alternative models (Alexnet and AE-Alexnet). Overall, the study contributes valuable insights into optimizing machining parameters and cooling strategies for enhanced surface finish in additively manufactured alloys.en_US
dc.description.sponsorshipPolish Natonal Agency For Academic Exchange (NAWA) [BPN/ULM/2023/1/00094/U/00001]en_US
dc.description.sponsorshipThe author Vishal S Sharma would like to thanks Polish Natonal Agency For Academic Exchange (NAWA) No. BPN/ULM/2023/1/00094/U/00001 for financial support.en_US
dc.identifier.doi10.1016/j.measurement.2024.114515
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-85188122559en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2024.114515
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5109
dc.identifier.volume230en_US
dc.identifier.wosWOS:001220297200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMeasurementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectMeasurementen_US
dc.subjectMHA-Alexneten_US
dc.subjectSurface roughnessen_US
dc.titleA new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning modelsen_US
dc.typeArticleen_US

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