Simple machine learning allied with data-driven methods for monitoring tool wear in machining processes

dc.contributor.advisorhttps://orcid.org/0000-0001-5621-9536
dc.contributor.advisorhttps://link.springer.com/article/10.1007%2Fs00170-020-05785-x
dc.contributor.authorFARIAS, ADALTO DE
dc.contributor.authorALMEIDA, SÉRGIO LUIZ RABELO DE
dc.contributor.authorSergio Delijaicov
dc.contributor.authorSERIACOPI, VANESSA
dc.contributor.authorEd Claudio Bordinassi
dc.contributor.authorOrcidhttps://orcid.org/0000-0002-3915-3077
dc.date.accessioned2022-01-03T20:20:16Z
dc.date.available2022-01-03T20:20:16Z
dc.date.issued2020-08-01
dc.description.abstractThe aim of this work was to identify the occurrence of machine tool wear in carbide inserts applied in a machine turning center with two steel materials. Through the data collected with an open-source communication protocol during machining, eighty trials of twenty runs each were performed using central composite design experiments, resulting in a data set of eighty lines for each tested material. The data set consisted of forty lines with the tool wear condition and forty lines without. Machining parameters were set to be in the range of the usual industrial values. The cutting parameters in the machining process were cutting speed, feed rate, cutting depth, and cutting fluid applied in the abundance condition and without cutting fluid (dry machining). The collected data were the spindle motor load, X-axis motor load, and Z-axis motor load in terms of the percentage used. AISI P20 and AISI 1045 steels workpieces were tested with both new and worn inserts, and a flank tool wear of 0.3 mm was artificially induced by machining with the same material before the data collecting experiment. Two approaches were used in order to analyze the data and create the machine learning process (MLP), in a prior analysis. The collected data set was tested without any previous treatment, with an optimal linear associative memory (OLAM) neural network, and the results showed 65% correct answers in predicting tool wear, considering 3/4 of the data set for training and 1/4 for validating. For the second approach, statistical data mining methods (DMM) and data-driven methods (DDM), known as a self-organizing deep learning method, were employed in order to increase the success ratio of the model. Both DMM and DDM applied along with the MLP OLAM neural network showed an increase in hitting the right answers to 93.8%. This model can be useful in machine monitoring using Industry 4.0 concepts, where one of the key challenges in machining components is finding the appropriate moment for a tool change.
dc.description.firstpage2491
dc.description.lastpage2501
dc.description.volume109
dc.identifier.citationFARIAS, A. DE; ALMEIDA, S. L. R. DE; DELIJAICOV, S.; SERIACOPI, V.; BORDINASSI, E. C. Simple machine learning allied with data-driven methods for monitoring tool wear in machining processes. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (INTERNET), v. 109, p. 2491-2501, 2020.
dc.identifier.doi10.1007/s00170-020-05785-x
dc.identifier.issn1433-3015
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3500
dc.relation.ispartofINTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (INTERNET)
dc.rightsAcesso Restrito
dc.rights.licenseCreative Commons "Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY 4.0). Fonte: https://link.springer.com/article/10.1007%2Fs00170-020-05785-x. Acesso em: 03 de jan. 2022.
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectData-driven
dc.subjectTool wear
dc.subjectMachining
dc.titleSimple machine learning allied with data-driven methods for monitoring tool wear in machining processespt_BR
dc.typeArtigopt_BR
fei.scopus.citations22
fei.scopus.eid2-s2.0-85088870678
fei.scopus.updated2024-07-01
fei.source.urlhttps://link.springer.com/article/10.1007%2Fs00170-020-05785-x
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