Optimization of Process Parameters During End Milling and Prediction of Work Piece Temperature Rise

Abstract

During the machining processes, heat gets generated as a result of plastic deformation of metal and friction along the tool-chip and tool-work piece interface. In materials having high thermal conductivity, like aluminium alloys, large amount of this heat is absorbed by the work piece. This results in the rise in the temperature of the work piece, which may lead to dimensional inaccuracies, surface damage and deformation. So, it is needed to control rise in the temperature of the work piece. This paper focuses on the measurement, analysis and prediction of work piece temperature rise during the dry end milling operation of Al 6063. The control factors used for experimentation were number of flutes, spindle speed, depth of cut and feed rate. The Taguchi method was employed for the planning of experimentation and L 18 orthogonal array was selected. The temperature rise of the work piece was measured with the help of K-type thermocouple embedded in the work piece. Signal to noise (S/N) ratio analysis was carried out using the lower-the-better quality characteristics. Depth of cut was identified as the most significant factor affecting the work piece temperature rise, followed by spindle speed. Analysis of variance (ANOVA) was employed to find out the significant parameters affecting the work piece temperature rise. ANOVA results were found to be in line with the S/N ratio analysis. Regression analysis was used for developing empirical equation of temperature rise. The temperature rise of the work piece was calculated using the regression equation and was found to be in good agreement with the measured values. Finally, confirmation tests were carried out to verify the results obtained. From the confirmation test it was found that the Taguchi method is an effective method to determine optimised parameters for minimization of work piece temperature.

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

“…Nonproductive time has also been minimized by synchronizing rapid movements of cutting tool using hybrid GA during 2.5 D milling [56]. Yang et al [60] adopted GA-PSO technique to maximize the profit rate. GA-PSO found better than other GA developed earlier.…”

“…Gupta et al [56] Optimized machining parameters for minimizing unit cost using GA. Yang et al [60] Concluded that GA-PSO is better than other GA.…”

Multi Performance Optimizationof Shoulder Milling Process Parameters of AA6063 T6 Aluminium Alloy by Taguchi Based GRA

“…Bhirudand R.R. Gawande [11] determined the impact of heat generated during the machining of materials of high conductivity. Performed end milling on Al 6063 in absence of coolant.…”

Influence of the Cutting Strategy on the Temperature and Surface Flatness of the Workpiece in Face Milling

“…The literature includes studies aiming to simulate [ 15 , 16 ] and optimize [ 17 ] the cutting process so that the excess heat generated at the tool−workpiece interface was reduced. Some research, e.g., [ 16 ], was based on finite difference analysis (FDA) to predict changes in the temperature and thermal resistance at the tool−chip interface.…”