<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper presents the use of Taguchi method to optimize the cutting of stainless steel by Abrasive Water Jet. Shown are the influence of the most important machining parameters, such a traverse speed, abrasive grains size and concentration of abrasive in the jet on the maximum depth of cut. Analysis of variance - ANOVA was used to determine the effect of machining parameters on the cutting depth. Based on the calculated signal/noise ratios for individual parameters of the cutting process, their impact on cutting depth was determined and optimal process conditions were determined in order to reach the maximum depth of cut. The empirical verification of this process was also performed by comparing the depth of cut predicted and achieved in the tests.</p></abstract>

The paper presents the use of Taguchi method to optimize the cutting of stainless steel by Abrasive Water Jet. Shown are the influence of the most important machining parameters, such a traverse speed, abrasive grains size and concentration of abrasive in the jet on the maximum depth of cut. Analysis of variance - ANOVA was used to determine the effect of machining parameters on the cutting depth. Based on the calculated signal/noise ratios for individual parameters of the cutting process, their impact on cutting depth was determined and optimal process conditions were determined in order to reach the maximum depth of cut. The empirical verification of this process was also performed by comparing the depth of cut predicted and achieved in the tests.

Abrasive Water Jet Cutting of Stainless-Steel Optimization by Orthogonal Array Approach

Acta Universitatis Cibiniensis. Technical Series

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The paper presents the use of Taguchi method to optimize the cutting of stainless steel by Abrasive Water Jet. Shown are the influence of the most important...

Abrasive Water Jet Cutting of Stainless-Steel Optimization by Orthogonal Array Approach

Data publikacji: 31 gru 2019

Zakres stron: 55 - 61

DOI: https://doi.org/10.2478/aucts-2019-0011

© 2019 Andrzej Perec et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.