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dc.contributor.authorKüçükyıldız, Gürkan
dc.contributor.authorDemir, Habibe Gürsoy
dc.date.accessioned2021-06-01T12:18:09Z
dc.date.available2021-06-01T12:18:09Z
dc.date.issued2021en_US
dc.identifier.citationKucukyildiz, G., Demir, H.G. (2021). A Multistage Cutting Tool Fault Diagnosis Algorithm for the Involute form Cutter Using Cutting Force and Vibration Signals Spectrum Imaging and Convolutional Neural Networks. Arab J Sci Eng. https://doi.org/10.1007/s13369-021-05709-1en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-021-05709-1
dc.identifier.urihttps://hdl.handle.net/20.500.12508/1719
dc.description.abstractIn a machining system, tool condition monitoring systems are required to get a high-quality product and to prevent the downtime of machine tools due to tool failures. For this purpose, tool condition monitoring systems have become very important during the years since the mechanical faults can cause high cost. This study introduces a multistage cutting tool fault diagnosis method to detect the presence and level of the involute form cutter faults on the by the cutting force and vibration signal analysis. Therefore, different fault levels (low, medium and high) were generated on the involute form cutter as a tool breakage. During the experiments, the cutting force, vibration and acoustic signals were gathered with three different feed rates for each fault level. The gathered signals were processed by a multistage signal processing algorithm developed in the MATLAB environment. As an initial step, the continuous wavelet transform of the obtained signals was taken and saved as an image by the developed algorithm. After that, a convolutional neural network model is trained and tested by using the obtained images. The developed algorithm firstly checks the presence of the cutting tool fault. Once the algorithm labels the cutting tool is damaged, it then checks the damage level of the cutting tool fault. It is observed from the results, cutting force analysis is sufficient for the detection of cutting tool fault. On the other hand, the cutting force signal analysis is insufficient to detect the damage level of the cutting tool. Therefore, the vibration signal analysis is required to detect the damage level of the cutting tool. Results prove that, by the vibration analysis, the developed algorithm could detect not only the presence of the damage on the cutting tool but also the damage level. The results of the algorithm for each stage and signal are given in the results section.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s13369-021-05709-1en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCutting tool breakage faulten_US
dc.subjectContinuous waveleten_US
dc.subjectDeep learningen_US
dc.subjectCNNen_US
dc.subjectCutting forceen_US
dc.subject.classificationMultidisciplinary Sciences
dc.subject.otherBreakage
dc.titleA Multistage Cutting Tool Fault Diagnosis Algorithm for the Involute form Cutter Using Cutting Force and Vibration Signals Spectrum Imaging and Convolutional Neural Networksen_US
dc.typearticleen_US
dc.relation.journalArabian Journal for Science and Engineeringen_US
dc.contributor.departmentHavacılık ve Uzay Bilimleri Fakültesi -- Havacılık ve Uzay Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0002-7705-9516en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorDemir, Habibe Gürsoy
dc.relation.indexWeb of Scienceen_US
dc.relation.indexWeb of Science Core Collection - Science Citation Index Expanded


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