Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys

The aim of this paper is to conduct an experimental study in order to obtain a roughness (Ra) prediction model for dry end-milling (with an AlTiCrSiN PVD-coated tool) of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni biomedical alloys, a model that can contribute to more quickly obtaining the desired surface...

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Autores principales:	Manuela-Roxana Dijmărescu, Bogdan Felician Abaza, Ionelia Voiculescu, Maria-Cristina Dijmărescu, Ion Ciocan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	roughness prediction biomedical alloys machining Co–28Cr–6Mo Co–20Cr–15W–10Ni ANN model AlTiCrSiN PVD coated tool Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/75d6659a8ab9473fbefcfb52e3cc35c0
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spelling	oai:doaj.org-article:75d6659a8ab9473fbefcfb52e3cc35c02021-11-11T17:55:46ZSurface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys10.3390/ma142163611996-1944https://doaj.org/article/75d6659a8ab9473fbefcfb52e3cc35c02021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6361https://doaj.org/toc/1996-1944The aim of this paper is to conduct an experimental study in order to obtain a roughness (Ra) prediction model for dry end-milling (with an AlTiCrSiN PVD-coated tool) of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni biomedical alloys, a model that can contribute to more quickly obtaining the desired surface quality and shortening the manufacturing process time. An experimental plan based on the central composite design method was adopted to determine the influence of the axial depth of cut, feed per tooth and cutting speed process parameters (input variables) on the Ra surface roughness (response variable) which was recorded after machining for both alloys. To develop the prediction models, statistical techniques were used first and three prediction equations were obtained for each alloy, the best results being achieved using response surface methodology. However, for obtaining a higher accuracy of prediction, ANN models were developed with the help of an application made in LabView for roughness (Ra) prediction. The primary results of this research consist of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni prediction models and the developed application. The modeling results show that the ANN model can predict the surface roughness with high accuracy for the considered Co–Cr alloys.Manuela-Roxana DijmărescuBogdan Felician AbazaIonelia VoiculescuMaria-Cristina DijmărescuIon CiocanMDPI AGarticleroughness predictionbiomedical alloys machiningCo–28Cr–6MoCo–20Cr–15W–10NiANN modelAlTiCrSiN PVD coated toolTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6361, p 6361 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	roughness prediction biomedical alloys machining Co–28Cr–6Mo Co–20Cr–15W–10Ni ANN model AlTiCrSiN PVD coated tool Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	roughness prediction biomedical alloys machining Co–28Cr–6Mo Co–20Cr–15W–10Ni ANN model AlTiCrSiN PVD coated tool Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Manuela-Roxana Dijmărescu Bogdan Felician Abaza Ionelia Voiculescu Maria-Cristina Dijmărescu Ion Ciocan Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys
description	The aim of this paper is to conduct an experimental study in order to obtain a roughness (Ra) prediction model for dry end-milling (with an AlTiCrSiN PVD-coated tool) of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni biomedical alloys, a model that can contribute to more quickly obtaining the desired surface quality and shortening the manufacturing process time. An experimental plan based on the central composite design method was adopted to determine the influence of the axial depth of cut, feed per tooth and cutting speed process parameters (input variables) on the Ra surface roughness (response variable) which was recorded after machining for both alloys. To develop the prediction models, statistical techniques were used first and three prediction equations were obtained for each alloy, the best results being achieved using response surface methodology. However, for obtaining a higher accuracy of prediction, ANN models were developed with the help of an application made in LabView for roughness (Ra) prediction. The primary results of this research consist of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni prediction models and the developed application. The modeling results show that the ANN model can predict the surface roughness with high accuracy for the considered Co–Cr alloys.
format	article
author	Manuela-Roxana Dijmărescu Bogdan Felician Abaza Ionelia Voiculescu Maria-Cristina Dijmărescu Ion Ciocan
author_facet	Manuela-Roxana Dijmărescu Bogdan Felician Abaza Ionelia Voiculescu Maria-Cristina Dijmărescu Ion Ciocan
author_sort	Manuela-Roxana Dijmărescu
title	Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys
title_short	Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys
title_full	Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys
title_fullStr	Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys
title_full_unstemmed	Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys
title_sort	surface roughness analysis and prediction with an artificial neural network model for dry milling of co–cr biomedical alloys
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/75d6659a8ab9473fbefcfb52e3cc35c0
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Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys

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