Proper estimation of surface roughness using hybrid intelligence based on artificial neural network and genetic algorithm

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dc.contributor.authorBoga, Cem
dc.contributor.authorKoroglu, Tahsin
dc.date.accessioned2025-01-06T17:37:00Z
dc.date.available2025-01-06T17:37:00Z
dc.date.issued2021
dc.description.abstractThe surface roughness is a crucial index that is commonly used in the machining process to evaluate the final product quality. This paper investigates the effect of different machining parameters on the surface roughness of the high-strength carbon fiber composite plate, manufactured by utilizing the vacuum infusion process, under dry end milling conditions. Besides, a hybrid intelligence approach consisting of artificial neural network (ANN) whose parameters are tuned by genetic algorithm (GA) is introduced for accurate estimation of surface rough-ness. To construct a database for the ANN, the experimental milling tests have been carried out according to the Taguchi optimization method with the design of a mixed orthogonal array L-32 (2(1) x 4(2)). The influence of the machining parameters such as cutting tools, feed rate, and spindle speed on surface roughness have been examined by using analysis of variance (ANOVA). The analyses reveal that the cutting tool and the feed rate are the most effective factors in the surface roughness of the composite material. It is also determined that the experiment with A1B2C1 combination (TiAlN coated cutting tool, 5000 rpm spindle speed, and 250 mm/rev feed rate) gives the optimal result. The proposed hybrid ANN-GA algorithm provides a good prediction correlation ratio (R = 0.96177) indicating that the estimated and the measured surface roughness values are remarkably close to each other. The mean square error (MSE) specifying the accuracy and adequacy of the network model is obtained as 0.074 during the 33th iteration of the GA.
dc.identifier.doi10.1016/j.jmapro.2021.08.062
dc.identifier.endpage569
dc.identifier.issn1526-6125
dc.identifier.issn2212-4616
dc.identifier.scopus2-s2.0-85114762378
dc.identifier.scopusqualityQ1
dc.identifier.startpage560
dc.identifier.urihttps://doi.org/10.1016/j.jmapro.2021.08.062
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2072
dc.identifier.volume70
dc.identifier.wosWOS:000702536700004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofJournal of Manufacturing Processes
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectCNC milling
dc.subjectCarbon fiber composite
dc.subjectSurface roughness
dc.subjectTaguchi design
dc.subjectArtificial neural network
dc.subjectGenetic algorithm
dc.titleProper estimation of surface roughness using hybrid intelligence based on artificial neural network and genetic algorithm
dc.typeArticle

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