Modeling and optimization of face milling process parameters for AISI 4140 steel

Abstract

In this study, the effect of cutting parameters such as the depth of cut, feed rate, cutting speed and the number of inserts on surface roughness were investigated in the milling of the AISI 4140 steel. The optimal control factors for surface quality were detected by using the Taguchi technique. Experimental trials were designed according to the Taguchi L18 (21x33) orthogonal array. The statistical effects of control factors on surface roughness have been established by using the analysis of variance (ANOVA). Optimal cutting parameters were obtained by using the S/N ratio values. The ANOVA results showed that the effective factors were the number of inserts and the feed rate on surface roughness. However, the depth of cut and the cutting speed showed an insignificant effect. Additionally, the First-order and Second-order regression analysis were conducted to estimate the performance characteristics of the experiment. The acquired regression equation results matched with the surface roughness measurement results. The optimal performance characteristics were obtained as a 0.5 mm depth of cut, 0.08 mm/rev feed rate, 325 m/min cutting speed and 1 number of inserts by using the Taguchi method. Additionally, the confirmation test results indicated that the Taguchi method was very prosperous in the optimization of the machining parameters to obtain the minimum surface roughness in the milling of the AISI 4140 steel.

Optimization and Modeling of Radial Pitch Diameter Difference in Tapping of AISI H13

“…Advances in Materials Science and Engineering where η G is the S/N calculated at the optimal level, η m is the average S/N of all experiments in Table 5, η i is the average S/N at the optimal level of each factor, and k is the number of factors that affect RPDD [23].…”

Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis

“…Analysis of variance method 25 is applied to determine the influence coefficient and contribution rate of cutting speed, cutting depth, feed rate and cutting width on surface roughness and tool wear. The analysis is conducted at 95% confidence level, and the calculation results are shown in Table 6.…”

Study on model for cutting force when milling SCM440 steel

“…Study on material type machining technology has been carried out by a number of authors, such as: surveying the flatness of the tool when milling [16], determining the optimal value of cutting parameters when turning to ensure minimum value of surface roughness [17], determining optimal cutting parameters when turning to ensure minimum value of surface roughness and tool wear [18], study on drilling technology this material when using a number of different cooling methods [19]. Study on milling equivalent steels of this steel has also been carried out by a number of studies, such as: building surface roughness models and determining the optimal value of cutting parameters to ensure that surface roughness has the smallest value when milling with TiAlN+TiN coated cutting tool [20], studying the efficiency of using coolant when milling with TiAlN coated cutting tools [21], surveying on surface roughness when milling with cutting tools made of CBN [22], comparing cutting force, tool wear and surface roughness when milling with 5 types of cutting tools coated with different materials (WC-Co-TiC, Al 2 O 3 -TiC, Ti(C, N), Ti(C, N)-WC-Mo 2 C-Ni-Co, and Ti(C, N)-WC-Mo 2 C-Co) [23]. This study has determined that out of five types of cutting tool materials, Ti (C, N) coated cutting tools have the highest efficiency.…”