Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable Steel

This article presents the results of tests aimed at determining the effect of slide burnishing parameters on the surface roughness of shafts made of 42CrMo4 heat-treatable steel. The burnishing process was carried out using tools with polycrystalline diamond and cemented carbide tips. Before burnish...

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Main Authors: Rafał Kluz, Katarzyna Antosz, Tomasz Trzepieciński, Magdalena Bucior
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Materials
Subjects:
Online Access:https://www.mdpi.com/1996-1944/14/5/1175
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author Rafał Kluz
Katarzyna Antosz
Tomasz Trzepieciński
Magdalena Bucior
author_facet Rafał Kluz
Katarzyna Antosz
Tomasz Trzepieciński
Magdalena Bucior
author_sort Rafał Kluz
collection DOAJ
description This article presents the results of tests aimed at determining the effect of slide burnishing parameters on the surface roughness of shafts made of 42CrMo4 heat-treatable steel. The burnishing process was carried out using tools with polycrystalline diamond and cemented carbide tips. Before burnishing, the samples were turned on a turning lathe to produce samples with an average surface roughness <i>Ra</i> = 2.6 µm. The investigations were carried out according to three-leveled Hartley’s poly selective quasi D (PS/DS-P: Ha3) plan, which enables a regression equation in the form of a second-order polynomial to be defined. Artificial neural network models were also used to predict the roughness of the surface of the shafts after slide burnishing. The input parameters of the process that were taken into account included the values of pressure, burnishing speed and feed rate. Overall, the burnishing process examined leads to a reduction in the value of the surface roughness described by the <i>Ra</i> parameter. The artificial neural networks with the best regression statistics predicted an average surface roughness of the shafts with <i>R</i><sup>2</sup> = 0.987. The lowest root-mean-square error and mean absolute error were obtained with all the network structures analysed that were trained with the quasi Newton algorithm.
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spelling doaj.art-7a3e1854097148c2a8f4f8c0858ad5742023-12-03T12:14:52ZengMDPI AGMaterials1996-19442021-03-01145117510.3390/ma14051175Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable SteelRafał Kluz0Katarzyna Antosz1Tomasz Trzepieciński2Magdalena Bucior3Department of Manufacturing and Production Engineering, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, PolandDepartment of Manufacturing and Production Engineering, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, PolandDepartment of Materials Forming and Processing, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, PolandDepartment of Manufacturing and Production Engineering, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, PolandThis article presents the results of tests aimed at determining the effect of slide burnishing parameters on the surface roughness of shafts made of 42CrMo4 heat-treatable steel. The burnishing process was carried out using tools with polycrystalline diamond and cemented carbide tips. Before burnishing, the samples were turned on a turning lathe to produce samples with an average surface roughness <i>Ra</i> = 2.6 µm. The investigations were carried out according to three-leveled Hartley’s poly selective quasi D (PS/DS-P: Ha3) plan, which enables a regression equation in the form of a second-order polynomial to be defined. Artificial neural network models were also used to predict the roughness of the surface of the shafts after slide burnishing. The input parameters of the process that were taken into account included the values of pressure, burnishing speed and feed rate. Overall, the burnishing process examined leads to a reduction in the value of the surface roughness described by the <i>Ra</i> parameter. The artificial neural networks with the best regression statistics predicted an average surface roughness of the shafts with <i>R</i><sup>2</sup> = 0.987. The lowest root-mean-square error and mean absolute error were obtained with all the network structures analysed that were trained with the quasi Newton algorithm.https://www.mdpi.com/1996-1944/14/5/1175average surface roughnessplastic workingslide burnishingsteel shaftsurface topography
spellingShingle Rafał Kluz
Katarzyna Antosz
Tomasz Trzepieciński
Magdalena Bucior
Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable Steel
Materials
average surface roughness
plastic working
slide burnishing
steel shaft
surface topography
title Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable Steel
title_full Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable Steel
title_fullStr Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable Steel
title_full_unstemmed Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable Steel
title_short Modelling the Influence of Slide Burnishing Parameters on the Surface Roughness of Shafts Made of 42CrMo4 Heat-Treatable Steel
title_sort modelling the influence of slide burnishing parameters on the surface roughness of shafts made of 42crmo4 heat treatable steel
topic average surface roughness
plastic working
slide burnishing
steel shaft
surface topography
url https://www.mdpi.com/1996-1944/14/5/1175
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