Configuration of tool wear and its mechanism in sustainable machining of titanium alloys with energy signals

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dc.contributor.authorVashishtha, Govind
dc.contributor.authorChauhan, Sumika
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorZimroz, Radoslaw
dc.contributor.authorKrolczyk, Grzegorz M.
dc.date.accessioned2024-09-29T15:51:01Z
dc.date.available2024-09-29T15:51:01Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractSurface quality, machining efficiency, and tool life are all significantly impacted by tool wear in metal cutting machining. Research priorities and areas of focus in tool wear are shifting as intelligent machining becomes the norm. Unfortunately, there are currently no acknowledged most effective ways for analyzing tool based on the energy signals specially in the machining of titanium and its alloys. In the present work, the titanium machining was performed under different lubrication conditions such as dry, minimum quantity lubrication (MQL), liquid nitrogen and hybrid, etc. Then, the spectrograms are used to transform the acquired energy data into time-frequency features. Starting with a set of randomly generated hyper parameters (HPs), the long short-term memory (LSTM) model is fine-tuned using sine cosine algorithm (SCA) with loss serving as the fitness function. The confusion matrix provides additional validation of the 98.08% classification accuracy. Additional evaluations of the suggested method's superiority include its specificity, sensitivity, F1-score, and area under the curve (AUC).en_US
dc.identifier.doi10.1007/s00170-024-14336-7
dc.identifier.endpage3573en_US
dc.identifier.issn0268-3768
dc.identifier.issn1433-3015
dc.identifier.issue7-8en_US
dc.identifier.scopus2-s2.0-85203082944en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3561en_US
dc.identifier.urihttps://doi.org/10.1007/s00170-024-14336-7
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3831
dc.identifier.volume134en_US
dc.identifier.wosWOS:001311863300018en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTool wearen_US
dc.subjectMachine learningen_US
dc.subjectSignal processingen_US
dc.subjectData acquisitionen_US
dc.subjectEnergy signalsen_US
dc.titleConfiguration of tool wear and its mechanism in sustainable machining of titanium alloys with energy signalsen_US
dc.typeArticleen_US

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