Multi-Objectives Optimization of Abrasive Water Jet Machining (AJWM) on Mild Steel
Abstract
Abrasive waterjet machining (AWJM) is an advanced machining technology that is commonly used to machine hard materials that are difficult to machine using traditional methods. AWJM with a narrow stream of high-velocity water and abrasive particles offers a low-cost and environmentally friendly machining approach with a high rate of material removal. Some issues that were usually highlighted while cutting the metal are poor appearance cutting due to visible stream lagging particularly when working at high-speed cutting. This can lead to decreased accuracy and precision in the cutting process. Past literature is mostly focused on improving the machining performances through intensive experimental works, thereby not many studies are concerned on process optimization through design of experiment approach. In this regard, this study aims to statically analyze how the controlled machining factors; transverse speed and cutting geometry influence surface roughness, and dimensional accuracy of a mild steel plate under the AWJC process. A two level Full Factorial method was applied to design the experiment that entailed 6 sets of parameters. Through the Analysis of Variance (ANOVA) on the experimental results, it was found that the dimensional accuracy is significantly influenced by the changes of cutting geometry. The factor also interacts with transverse speed to affect surface roughness. For optimization, the ANOVA suggest a transverse speed of 40% as the optimum value to produce a surface at 2.85 µm of roughness and a dimension accuracy of 0.177% for the circular geometry-controlled factor.
Downloads
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Integrated Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Open access licenses
Open Access is by licensing the content with a Creative Commons (CC) license.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.