Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi-Based Response Surface Methodology

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left">Input parameters</td><td class="align_center">Level 1</td><td class="align_center">Level 2</td><td class="align_center">Level 3</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">Cutting speed (rpm)</td><td class="align_center">90</td><td class="align_center">270</td><td class="align_center">450</td></tr><tr><td class="align_left">Depth of cut (mm)</td><td class="align_center">0.2</td><td class="align_center">0.4</td><td class="align_center">0.6</td></tr><tr><td class="align_left">Type of cutting tool</td><td class="align_center">HSST</td><td class="align_center">HSST-TiN</td><td class="align_center">HSST-TiAlN</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Input parameters and their levels.</div>

Advances in Materials Science and Engineering

tab4

Table 4

Table 4: Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi-Based Response Surface Methodology 