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The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy

This paper analyses the effect of the abrasive waterjet cutting parameters’ modification on the condition of the workpiece surface layer. The post-machined surface of casting aluminium alloys, AlSi10Mg and AlSi21CuNi, was characterised in terms of surface roughness and irregularities, chamfering, an...

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Autores principales: Kulisz, Monika, Zagórski, Ireneusz, Korpysa, Jarosław
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411828/
https://www.ncbi.nlm.nih.gov/pubmed/32668746
http://dx.doi.org/10.3390/ma13143122
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author Kulisz, Monika
Zagórski, Ireneusz
Korpysa, Jarosław
author_facet Kulisz, Monika
Zagórski, Ireneusz
Korpysa, Jarosław
author_sort Kulisz, Monika
collection PubMed
description This paper analyses the effect of the abrasive waterjet cutting parameters’ modification on the condition of the workpiece surface layer. The post-machined surface of casting aluminium alloys, AlSi10Mg and AlSi21CuNi, was characterised in terms of surface roughness and irregularities, chamfering, and microhardness in order to reveal the effect that variable jet feed rate, abrasive flow rate, and sample height (thickness of the cut material) have on the quality of surface finish. From the analysis of the results, it emerges that the surface roughness remains largely unaffected by changes in the sample height h or the abrasive flow rate m(a), whereas it is highly susceptible to the increase in the jet feed rate v(f). It has been shown that, in principle, the machining does not produce the strengthening effect, that is, an increase in microhardness. Owing to the irregularities that are typically found on the workpieces cut with higher jet feed rates v(f), additional surface finish operations may prove necessary. In addition, chamfering was found to occur throughout the entire range of speeds v(f). The statistical significance of individual variables on the 2D surface roughness parameters, Ra/Rz/RSm, was determined using factorial analysis of variance (ANOVA). The results were verified by means of artificial neural network (ANN) modelling (radial basis function and multi-layered perceptron), which was employed to predict the surface roughness parameters under consideration. The obtained correlation coefficients show that ANNs exhibit satisfying predictive capacity, and are thus a suitable tool for the prediction of surface roughness parameters in abrasive waterjet (AWJ) technology.
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spelling pubmed-74118282020-08-25 The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy Kulisz, Monika Zagórski, Ireneusz Korpysa, Jarosław Materials (Basel) Article This paper analyses the effect of the abrasive waterjet cutting parameters’ modification on the condition of the workpiece surface layer. The post-machined surface of casting aluminium alloys, AlSi10Mg and AlSi21CuNi, was characterised in terms of surface roughness and irregularities, chamfering, and microhardness in order to reveal the effect that variable jet feed rate, abrasive flow rate, and sample height (thickness of the cut material) have on the quality of surface finish. From the analysis of the results, it emerges that the surface roughness remains largely unaffected by changes in the sample height h or the abrasive flow rate m(a), whereas it is highly susceptible to the increase in the jet feed rate v(f). It has been shown that, in principle, the machining does not produce the strengthening effect, that is, an increase in microhardness. Owing to the irregularities that are typically found on the workpieces cut with higher jet feed rates v(f), additional surface finish operations may prove necessary. In addition, chamfering was found to occur throughout the entire range of speeds v(f). The statistical significance of individual variables on the 2D surface roughness parameters, Ra/Rz/RSm, was determined using factorial analysis of variance (ANOVA). The results were verified by means of artificial neural network (ANN) modelling (radial basis function and multi-layered perceptron), which was employed to predict the surface roughness parameters under consideration. The obtained correlation coefficients show that ANNs exhibit satisfying predictive capacity, and are thus a suitable tool for the prediction of surface roughness parameters in abrasive waterjet (AWJ) technology. MDPI 2020-07-13 /pmc/articles/PMC7411828/ /pubmed/32668746 http://dx.doi.org/10.3390/ma13143122 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kulisz, Monika
Zagórski, Ireneusz
Korpysa, Jarosław
The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy
title The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy
title_full The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy
title_fullStr The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy
title_full_unstemmed The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy
title_short The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy
title_sort effect of abrasive waterjet machining parameters on the condition of al-si alloy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411828/
https://www.ncbi.nlm.nih.gov/pubmed/32668746
http://dx.doi.org/10.3390/ma13143122
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