Classification of CNC Vibration Speeds by Heralick Features

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dc.authoridMINAZ, Mehmet Recep/0000-0001-8046-6465
dc.authoridKaplan, Kaplan/0000-0001-8036-1145
dc.contributor.authorKuncan, Melih
dc.contributor.authorKaplan, Kaplan
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorMinaz, Mehmet Recep
dc.contributor.authorErtunc, H. Metin
dc.date.accessioned2024-12-24T19:33:57Z
dc.date.available2024-12-24T19:33:57Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractIn the contemporary landscape of industrial manufacturing, the concept of computer numerical control (CNC) has emerged due to the optimization of conventional machinery, distinguished by its remarkable precision and expeditious processing capabilities. These inherent advantages have seamlessly paved the way for the pervasive integration of CNC machines across a myriad of industrial manufacturing sectors. The present study embarks upon a comprehensive inquiry, delving into the intricate analysis of a specialized prototype CNC molding machine, encompassing a meticulous assessment of its structural rigidity, robustness, and propensity for vibrational occurrences. Moreover, an insightful exploration is undertaken to discern the intricate interplay between vibrational signals and intricate machining processes, particularly under diverse conditions such as the presence or absence of the cutting tool, and at varying rotational speeds denoted in revolutions per minute (RPM). The trajectory of this research voyage encompasses an extensive array of empirical experiments meticulously conducted on the prototype CNC machine, with synchronous real-time acquisition of vibrational data. This empirical journey starts by generating two distinct datasets, each meticulously designed to encompass an assemblage of seven distinct rotational speeds, spanning the spectrum from 18000 to 30000 RPM, thereby facilitating enhanced diversity within the dataset. In parallel, a secondary dataset is meticulously derived from the CNC machine operating in the absence of the cutting tool, thereby encapsulating an exhaustive range of 20 discrete RPM values. The extraction of pivotal features aimed at discerning between the vibrational signals arising from distinct conditions (i.e., those emanating from situations involving the presence or absence of the cutting tool) and the associated variance in CNC machine speeds is facilitated through an innovative framework grounded in co -occurrence matrices. The culmination of this methodological framework is the identification of discernible co -occurrence matrices, thereby facilitating the subsequent computation of Heralick features. The classification effort was performed systematically using 10 -fold cross -validation analysis, covering a number of different machine learning models. The outcomes emanating from this intricate sequence of systematic methodologies underscore remarkable achievements. Specifically, the classification of vibrational signals corresponding to varying CNC machine speeds, contingent upon the presence or absence of the cutting tool, yields commendable accuracy rates of 94.27% and 94.16%, respectively. Notably, an exemplary accuracy rate of 100% is attained when classifying differing conditions (i.e., situations involving the presence or absence of the cutting tool) across specific RPM settings, prominently at 22000 24000 26000 28000 and 30000 RPM.
dc.description.sponsorshipRepublic of Turkey, Ministry of Science, Industry and Technology [0577.STZ.2013-2]
dc.description.sponsorshipThis work was supported by the Republic of Turkey, Ministry of Science, Industry and Technology under project code 0577.STZ.2013-2.
dc.identifier.doi10.3897/jucs.106543
dc.identifier.endpage382
dc.identifier.issn0948-695X
dc.identifier.issn0948-6968
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85189107181
dc.identifier.scopusqualityQ3
dc.identifier.startpage363
dc.identifier.urihttps://doi.org/10.3897/jucs.106543
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8341
dc.identifier.volume30
dc.identifier.wosWOS:001245820400005
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherGraz Univ Technolgoy, Inst Information Systems Computer Media-Iicm
dc.relation.ispartofJournal of Universal Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectCNC
dc.subjectclassification
dc.subjectHeralick features
dc.subjectmachine learning
dc.subjectvibration signal
dc.titleClassification of CNC Vibration Speeds by Heralick Features
dc.typeArticle

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