An Integrated Approach of RSM and MOGA for the Prediction of Temperature Rise and Surface Roughness in the End Milling of Al 6061-T6

Abstract

Cutting temperature, machining parameters, workpiece material, and cutting tool geometry have a significant influence on the achievement of the desired quality of product at a satisfactory cost. The aim of the present study was to develop an empirical model for predicting temperature rise (Tr) and surface roughness (Ra) in terms of spindle speed (N), feed rate (F), axial depth of cut (D a), radial depth of cut (D r), and radial rake angle (γ). The experiment was conducted on Al 6061-T6 by using a high-speed steel (HSS) end cutter based on the central composite design of response surface methodology (RSM). A second order mathematical model in terms of machining parameters was developed. The Analysis of Variance (ANOVA) was used to study the performance characteristics in the machining process. The values of Prob>F less than 0.05 indicate that the model terms are significant. The experimental results indicate that the formation of surface defect in the end milling of Al 6061-T6 results from the re-deposited tool material, plucking, feed marks, micro-pits, and chip layer formation. The high quality of the surface texture is obtained in the combined conditions of high spindle speed, optimal feed rate, lower axial and radial depths of cut, and radial rake angle. Multi objective genetic algorithm (MOGA) has been applied to optimize the machining parameters that simultaneously minimize temperature rise and surface roughness. A set of Pareto-optimal solutions provides flexibility to the manufacturer and the process engineer to select the best setting based on the quality requirements and applications. A verification and validation process shows that the predicted values were found to be in good agreement with the observed values.

The Optimization of Lathe Cutting Parameters Using a Hybrid Taguchi-Genetic Algorithm

“…The functions of the two objectives were conducted for normalization setting to prevent one from reaching the target value too fast as a result of the division of the maximum value. The original objective function was converted to the same range of output and then weighted to form a multi-objective function, as shown in the following formula(13), (14) and (15). () fx is the multiobjective function after the combination; 1 w and 2 w are the objective functions with the weighted pattern 1 2 1 ww  respectively.…”

“…The created prediction model was used to find the best solution based on the optimization objective, and different optimization algorithms can be seen in the literature. Zeelanbasha, et al [14] developed a prediction model for measurement of temperature rise and surface roughness. The multi-objective genetic algorithm (MOGA) found 18 sets of optimal processing parameters and confirmed the best processing parameters for achieving the minimum temperature rise and surface roughness.…”

An Improved Mathematical Model of Cutting Temperature in End Milling Al7050 Based on the Influence of Tool Geometry Parameters and Milling Parameters

“…Rana and Kumar [18] developed a mathematical model of the cutting temperature when using a tungsten carbide tool to turn EN19 steel by central composite design method. Using a central combination design method, Zeelanbasha et al [19] designed the cutting experiment of cutting Al6006-T6 with high-speed steel end milling and established a second-order mathematical model of dependency of cutting temperature on spindle speed, feed rate, axial cutting depth, radial depth of cut, and rake angle. Zheng et al [20] adopted the method of combining simulation and experimental verification to establish the 2D milling simulation model, studied the influence of milling parameters (feed rate, milling depth, spindle speed, milling width, and milling depth) on milling force and milling temperature during the milling process of 7075 aluminum alloy, then carried out milling parameter optimization.…”

Investigation on influence of cutting parameters on spindle vibration of CNC wood milling machine

“…Experimental investigation on the effect of cutting parameters on the spindle vibration and surface roughness in the precision end-milling process using the singular spectrum analysis [8][9][10]. An experimental investigation on the effect of machining and geometrical parameters such as spindle speed, feed rate, axial and radial depth of cut and radial rake angle on responses during end milling operation on the surface roughness and its defects of AL 6061-T6 [11]. Effect of tool vibration, cutting conditions on surface roughness during lathe dry turning process [12,13] Surface roughness is mainly affected by machine tool vibration.…”