Experimental investigation and optimization of wall deflection and material removal rate in milling thin-wall parts

Abstract

The selection of optimal process parameters is essential while machining thin-wall parts since it influences the quality of the product and affects productivity. Dimensional accuracy affects the product quality, whereas the material removal rate alters the process productivity. Therefore, the study investigated the effect of tool diameter, feed per tooth, axial and radial depth of cut on wall deflection, and material removal rate. The selected process parameters were found to significantly influence the in-process deflection and thickness deviation due to the generation of unfavorable cutting forces. Further, an increase in the material removal rate resulted in chatter, thus adversely affecting the surface quality during the final stages of machining. Considering the conflicting nature of the two performance measures, Non-dominated Sorting Genetic Algorithm-II was adopted to solve the multi-objective optimization problem. The developed model could predict the optimal combination of process variables needed to lower the in-process wall deflection and maintain a superior surface finish while maintaining a steady material removal rate.

Keywords

• thin–wall milling
• part deflection
• material removal rate
• aluminum alloy 2024-t351
• surface roughness
• multi-objective optimization
• nsga-ii