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

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dc.contributor.authorde Farias A.
dc.contributor.authorde Almeida S.L.R.
dc.contributor.authorDelijaicov S.
dc.contributor.authorSeriacopi V.
dc.contributor.authorBordinassi E.C.
dc.date.accessioned2024-03-12T23:47:12Z
dc.date.available2024-03-12T23:47:12Z
dc.date.issued2020
dc.description.abstract© 2020, Springer-Verlag London Ltd., part of Springer Nature.The 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.issuenumber9-12
dc.description.lastpage2501
dc.description.volume109
dc.identifier.doi10.1007/s00170-020-05785-x
dc.identifier.issn1433-3015
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34937
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technology
dc.rightsAcesso Restrito
dc.subject.otherlanguageData-driven
dc.subject.otherlanguageDeep learning
dc.subject.otherlanguageMachine learning
dc.subject.otherlanguageMachining
dc.subject.otherlanguageTool wear
dc.titleSimple machine learning allied with data-driven methods for monitoring tool wear in machining processes
dc.typeArtigo
local.scopus.citations21
local.scopus.eid2-s2.0-85088870678
local.scopus.subjectAssociative memory
local.scopus.subjectCentral composite designs
local.scopus.subjectCutting parameters
local.scopus.subjectData-driven methods
local.scopus.subjectMachine monitoring
local.scopus.subjectMachining parameters
local.scopus.subjectMachining Process
local.scopus.subjectStatistical datas
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85088870678&origin=inward
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