Tool wear monitoring in bandsawing using neural networks and Taguchi's design of experiments

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dc.contributor.authorSaglam, Haci
dc.date.accessioned2020-03-26T18:17:20Z
dc.date.available2020-03-26T18:17:20Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe bandsawing as a multi-point cutting operation is the preferred method for cutting off raw materials in industry. Although cutting off with bandsaw is very old process, research efforts are very limited compared to the other cutting process. Appropriate online tool condition monitoring system is essential for sophisticated and automated machine tools to achieve better tool management. Tool wear monitoring models using artificial neural network are developed to predict the tool wear during cutting off the raw materials (American Iron and Steel Institute 1020, 1040 and 4140) by bandsaw. Based on a continuous data acquisition of cutting force signals, it is possible to estimate or to classify certain wear parameters by means of neural networks thanks to reasonably quick data-processing capability. The multi-layered feed forward artificial neural network (ANN) system of a 6 x 9 x 1 structure based on cutting forces was trained using error back-propagation training algorithm to estimate tool wear in bandsawing. The data used for the training and checking of the network were derived from the experiments according to the principles of Taguchi design of experiments planned as L (27). The factors considered as input in the experiment were the feed rate, the cutting speed, the engagement length and material hardness. 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on sawblade. The analysis shows that cutting length, hardness and cutting speed have significant effect on tooth wear, respectively, while feed rate has less effect. In this study, the details of experimentation and ANN application to predict tooth wear have been presented. The system shows that there is close match between the flank wear estimated and measured directly.en_US
dc.description.sponsorship[2003/179]en_US
dc.description.sponsorshipThis experimental study was supported by a Scientific Research Projects, no. 2003/179. The author would also like to thank IMAS A. S. for providing the machine and materials for conducting the experiments.en_US
dc.identifier.doi10.1007/s00170-010-3133-1en_US
dc.identifier.endpage982en_US
dc.identifier.issn0268-3768en_US
dc.identifier.issn1433-3015en_US
dc.identifier.issue09.12.2020en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage969en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00170-010-3133-1
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27014
dc.identifier.volume55en_US
dc.identifier.wosWOS:000292162300012en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBandsawingen_US
dc.subjectCutting force measurementen_US
dc.subjectTool wearen_US
dc.subjectArtificial neural networken_US
dc.subjectTaguchi design of experimenten_US
dc.subjectSpecific cutting pressureen_US
dc.titleTool wear monitoring in bandsawing using neural networks and Taguchi's design of experimentsen_US
dc.typeArticleen_US

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