Manufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineering

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dc.contributor.authorDeák, Krisztián
dc.contributor.authorMenyhárt, József
dc.date.accessioned2024-07-22T20:22:12Z
dc.date.available2024-07-22T20:22:12Z
dc.date.issued2023-09-29
dc.description.abstractThe optimization of manufacturing and production processes with various computer software is essential these days. Solutions on the market allow us to optimize and improve our manufacturing and production processes; one of the most popular software is called Tecnomatrix, which is described in this paper. Tool condition monitoring is a vital part of the manufacturing process in the industry. It requires continuous measurement of the wear of the cutting tool edges to improve the surface quality of the work piece and maintain productivity. Multiple methods are available for the determination of the actual condition of the cutting tool. Vibration diagnostics and acoustic methods are included in this paper. These methods are simple, it requires only high sensitive sensors, microphones, and data acquisition unit to gather the vibration signal and make signal improvement. Extended Taylor equation is applied for tool edge wear ratio. Labview and Matlab software are applied for the measurement and the digital signal processing. Machine learning method with artificial neural network is for the detection and prediction of the edge wear to estimate the remaining useful lifetime (RUL) of the tool.en
dc.description.abstractThe optimization of manufacturing and production processes with various computer software is essential these days. Solutions on the market allow us to optimize and improve our manufacturing and production processes; one of the most popular software is called Tecnomatrix, which is described in this paper. Tool condition monitoring is a vital part of the manufacturing process in the industry. It requires continuous measurement of the wear of the cutting tool edges to improve the surface quality of the work piece and maintain productivity. Multiple methods are available for the determination of the actual condition of the cutting tool. Vibration diagnostics and acoustic methods are included in this paper. These methods are simple, it requires only high sensitive sensors, microphones, and data acquisition unit to gather the vibration signal and make signal improvement. Extended Taylor equation is applied for tool edge wear ratio. Labview and Matlab software are applied for the measurement and the digital signal processing. Machine learning method with artificial neural network is for the detection and prediction of the edge wear to estimate the remaining useful lifetime (RUL) of the tool.hu
dc.formatapplication/pdf
dc.identifier.citationInternational Journal of Engineering and Management Sciences, Vol. 8 No. 3 (2023) , 72-89
dc.identifier.doihttps://doi.org/10.21791/IJEMS.2023.026
dc.identifier.eissn2498-700X
dc.identifier.issue3
dc.identifier.jtitleInternational Journal of Engineering and Management Sciences
dc.identifier.urihttps://hdl.handle.net/2437/376150
dc.identifier.volume8
dc.languageen
dc.relationhttps://ojs.lib.unideb.hu/IJEMS/article/view/12332
dc.rights.accessOpen Access
dc.rights.ownerKrisztián Deák, József Menyhárt
dc.subjectTecnomatixen
dc.subjectplant simulationen
dc.subjecttool, wearen
dc.subjectdiagnosisen
dc.subjectmachine learningen
dc.subjectTecnomatixhu
dc.subjectplant simulationhu
dc.subjecttool, wearhu
dc.subjectdiagnosishu
dc.subjectmachine learninghu
dc.titleManufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineeringen
dc.typefolyóiratcikkhu
dc.typearticleen
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International Journal of Engineering and Management Sciences (DE-journals)