Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations

<table class="table-group" id="tab9"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Parameter</td><td class="align_center">Estimate</td><td class="align_center">SE estimate</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left"><i>x</i><sub>1</sub></td><td class="align_center">0.277470</td><td class="align_center">0.0820493</td></tr><tr><td class="align_left"><i>x</i><sub>2</sub></td><td class="align_center">0.486936</td><td class="align_center">0.0552161</td></tr><tr><td class="align_left"><i>x</i><sub>3</sub></td><td class="align_center">0.019772</td><td class="align_center">0.0419983</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr><tr class="table-fn"><td><div>Maximum iterations: 200; tolerance: 0.00001.<br/></div></td></tr></table>

<div>Nonlinear regression analysis carried out with the Levenberg–Marquardt algorithm.</div>

Mathematical Problems in Engineering

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Table 9

Table 9: Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations 

Table 9 | Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations 