Real-time monitoring and measurement of energy characteristics in sustainable machining of titanium alloys

Basit Öğe Kaydı
dc.authoridSIRIN, SENOL/0000-0002-3629-9003
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorYilmaz, Hakan
dc.contributor.authorSirin, Senol
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorJamil, Muhammad
dc.contributor.authorKrolczyk, Grzegorz M.
dc.date.accessioned2024-09-29T15:57:56Z
dc.date.available2024-09-29T15:57:56Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe development of cutting-edge monitoring technologies such as embedded devices and sensors has become necessary to ensure an industrial intelligence in modern manufacturing by recording machine, process, tool, and energy consumption conditions. Similarly, machine learning based real time systems are popular in the context of Industry 4.0 and are generally used for predicting energy needs and improving energy utilization efficiency and performance. In addition, sustainable and energy-efficient machining technologies that can reduce energy consumption and associated negative environmental effects have been the latest topic of much study in recent years. Concerning this regard, the present work firstly deals with the real time monitoring and measurement of energy characteristics while machining titanium alloys under dry, minimum quantity lubrication (MQL), liquid nitrogen (LN2) and hybrid (MQL + LN2) conditions. The energy characteristics at different stages of machine tools were monitored with the help of a high end energy analyser. Then, the energy signals from each stage of machining operation were predicted and classified with the help of different machine learning (ML) models. The experimental results showed that MQL, LN2, and hybrid conditions decreased the total energy consumption by averagely 2.6 %, 17.0 %, and 16.3 %, respectively, compared to dry condition. The ML results demonstrated that the accuracy of the random forest (RF) approach obtained higher efficacy with 96.3 % in all four conditions. In addition, it has been noticed that the hybrid cooling conditions are helpful in reducing the energy consumption values at different stages.en_US
dc.description.sponsorshipOpole University of Technology as part of the GRAS project [269/23]en_US
dc.description.sponsorshipThis work was financially supported by the Opole University of Technology as part of the GRAS project no. 269/23.en_US
dc.identifier.doi10.1016/j.measurement.2023.113937
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-85178634611en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2023.113937
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5108
dc.identifier.volume224en_US
dc.identifier.wosWOS:001154361000001en_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/openAccessen_US
dc.subjectEnergyen_US
dc.subjectMeasurementen_US
dc.subjectSensorsen_US
dc.subjectMachine learningen_US
dc.subjectReal time monitoringen_US
dc.titleReal-time monitoring and measurement of energy characteristics in sustainable machining of titanium alloysen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu