Modelling and Optimization of the Effects of Milling Parameters on Quality and Productivity, In a CNC Face Milling Operation

Abstract

This study focuses on the modelling and optimization of milling parameters to enhance quality and productivity in a CNC face milling operation. The objective is to investigate the influence of feed rate, depth of cut, width of cut, and spindle speed on material removal rate (MRR) and surface roughness (SR) using statistical modelling and optimization techniques. The purpose is to determine optimal parameter settings that maximize MRR while minimizing SR, thereby improving machining efficiency and product quality. Experimental runs were conducted based on a design matrix of thirty (30) factorial settings generated using Design Expert software. The experiments measured MRR and SR at different parameter levels, and the results were analyzed using Analysis of Variance (ANOVA). A quadratic model was developed to predict the responses, with R-squared values of 0.9983 for MRR and 0.9106 for SR, confirming the model's reliability. The ANOVA results indicated that feed rate, depth of cut, and width of cut significantly influenced MRR (p < 0.0001), while feed rate and depth of cut were the most significant factors affecting SR (p < 0.0001). Surface plots demonstrated the relationship between the input factors and responses, revealing that increasing feed rate and depth of cut enhances MRR but adversely affects SR. Optimization techniques identified optimal parameter settings that balanced both objectives. The study concludes that a well-optimized combination of milling parameters improves machining efficiency and surface quality, providing valuable insights for manufacturing industries seeking to optimize CNC face milling processes.