Measuring Surface Characteristics in Sustainable Machining of Titanium Alloys Using Deep Learning-Based Image Processing

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dc.authoridKORKMAZ, Mehmet Erdi/0000-0002-0481-6002
dc.authoridShibi, Sherin/0000-0002-7942-2438
dc.authoridGupta, Munish/0000-0002-0777-1559
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorShibi, C. Sherin
dc.contributor.authorMustafa, Sithara Mohamed
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorSharma, Vishal S.
dc.contributor.authorLi, Z.
dc.date.accessioned2024-09-29T16:04:29Z
dc.date.available2024-09-29T16:04:29Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractA crucial method of maintenance in the manufacturing industry is machine vision-based fault diagnostics and condition monitoring of machine tools. The friction that occurs between the tool and the workpiece has a greater influence on the surface properties of the material. Effective problem diagnosis is necessary for machine systems to continue operations safely. Data-driven approaches have recently exhibited great promise for intelligent fault diagnosis. Unfortunately, the data collected under real-world conditions may be imbalanced, making diagnosis difficult. In dry, minimum quantity lubrication (MQL), and cryogenic circumstances, the method of failure detection of the proposed design is novel. The purpose of this interrogation is to evaluate the roughness profiles obtained from the machined surfaces and class separation. Markov transition field (MTF) is adopted to encode the surface profiles. In addition to this, conditional generative adversarial network (CGAN) for augmentation and bidirectional long-short term memory (BLSTM), multilayer perceptron (MLP), and 2-D-convolutional neural network (CNN) models are used for surface profile classification and correlation with process parameters. According to the study's finding, the 2-D-CNN was significantly more accurate than the models in predicting surface profiles, with an average accuracy of above 99.6% in both training and testing. In the limelight, the suggested approach can demonstrate to be quite useful for categorizing and proposing appropriate machining circumstances, specifically in situations with minimal data.en_US
dc.description.sponsorshipNorwegian Financial Mechanism (2014-2021) [2020/37/K/ST8/02795]; Polish National Agency for Academic Exchange (NAWA) [PPN/ULM/2020/1/00121]en_US
dc.description.sponsorshipThis work was supported in part by the Norwegian Financial Mechanism (2014-2021) under Contract 2020/37/K/ST8/02795 and in part by the Polish National Agency for Academic Exchange (NAWA) under Grant PPN/ULM/2020/1/00121. The associate editor coordinating the review of this article and approving it for publication was Dr. Yang Yang. (Corresponding authors: Munish Kumar Gupta; Z. Li.)en_US
dc.identifier.doi10.1109/JSEN.2023.3269529
dc.identifier.endpage13639en_US
dc.identifier.issn1530-437X
dc.identifier.issn1558-1748
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85159675561en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage13629en_US
dc.identifier.urihttps://doi.org/10.1109/JSEN.2023.3269529
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6151
dc.identifier.volume23en_US
dc.identifier.wosWOS:001014626700120en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Sensors Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSurface treatmenten_US
dc.subjectMachiningen_US
dc.subjectSurface morphologyen_US
dc.subjectMarkov processesen_US
dc.subjectSurface roughnessen_US
dc.subjectRough surfacesen_US
dc.subjectPredictive modelsen_US
dc.subjectConditional generative adversarial network (CGAN)en_US
dc.subjectcryogenicen_US
dc.subjectdeep learning (DL)en_US
dc.subjectmachiningen_US
dc.subjectMarkov transition field (MTF)en_US
dc.titleMeasuring Surface Characteristics in Sustainable Machining of Titanium Alloys Using Deep Learning-Based Image Processingen_US
dc.typeArticleen_US

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