Optimizing Cutting Conditions for Minimum Surface Roughness in Face Milling of High Strength Steel Using Carbide Inserts

Abstract

A full factorial design technique is used to investigate the effect of machining parameters, namely, spindle speed(N), depth of cut(ap),and table feed rate(Vf),on the obtained surface roughness (RaandRt) during face milling operation of high strength steel. A second-order regression model was built using least squares method depending on the factorial design results to approximate a mathematical relationship between the surface roughness and the studied process parameters. Analysis of variance was conducted to estimate the significance of each factor and interaction with respect to the surface roughness. ForRa, the results show that spindle speed, depth of cut, and table feed rate have a significant effect on the surface roughness in both linear and quadratic terms. There is also an interaction between depth of cut and feed rate. It also appears that feed rate has the greatest effect on the data variation followed by depth of cut. ForRt, the results show that the table feed rate is the most effective factor followed by the depth of cut, while the spindle speed had a significant small effect only in its quadratic term. The conditions of minimumRaandRtare identified through least square optimization. Moreover, multiobjective optimization for minimizingRaand maximizing metal removal rateQis conducted and the results are presented.

Taguchi Robust Design for Optimizing Surface Roughness of Turned AISI 1045 Steel Considering the Tool Nose Radius and Coolant as Noise Factors

“…It shows the increase of the feed rate and depth of cut were found to diminish the surface quality due the amount of forces and related friction involved in cutting larger material volume increments. Similar high speed rates and depth of cut e ects have been reported by other studies [11,12,15]. Composite desirability function was used to select the optimum combination of control factors that optimizes Ra under different noise factors conditions, i.e., the control factors levels that will minimize the effects of the noise factors.…”

Review on Optimization Techniques used for Determining Machining Conditions to get Effective Tool Life and Surface Finish

“…Adel Taha Abbas et al (2016) examined the effect of cutting speed, feed rate and depth of cut on surface roughness on high strength steel with carbide tools using ANOVA and regression analysis through DOE full factorial design. Factorial design was built using three independent variables with four levels of each factor.…”

Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel

“…Considering the above, let us now look at the studies that establish the optimal parameters of face milling using multi-objective optimization [31][32][33]. Fratila and Caizar [31] outlined the Taguchi optimization methodology, which is applied to optimize the cutting parameters in face milling when machining AlMg 3 with a high-speed steel (HSS) tool, in order to obtain the best surface roughness with minimum power consumption.…”