The aim of this study is to evaluate the impact of factors such as cutting speed, feed rate, and depth of cut on surface roughness and Material Removed Rate (MRR) when machining in dry face milling AISI 1040 steel with coated carbide inserts GC1030 using the response surface methodology (RSM). For this purpose, a number of machining experiments based on statistical three-factor and three-level factorial experiment designs, completed (L27) with a statistical analysis of variance (ANOVA), were performed in order to develop mathematical models and to identify the significant factors of these technological parameters. Multi-objective optimization procedure for minimizing Ra, Ry and Rz and maximizing MRR using desirability approach has been also implementented. The current study was also carried out to investigate the tool life of the inserts. The models found the relationship between the cutting parameters (Vc, fz and ap) and the studied technological parameters. It has been found that the cutting speed was the most affecting surface roughness which is due to the geometry of the insert which has a scraping edge and enables to obtain low roughness even at important feed rate, followed by the feed rate and the depth of cut at the end. The optimal combination of cutting parameters were cutting speed of 314 m/min, feed rate of 0.16 mm/tooth and depth of cut of 0.6 mm with a composite desirability of 0.924.
Inconel 718 is among difficult to machine materials because of its abrasiveness and high strength even at high temperature. This alloy is mainly used in aircraft and aerospace industries. Therefore, it is very important to reveal and evaluate cutting tools behavior during machining of this kind of alloy. The experimental study presented in this research work has been carried out in order to elucidate surface roughness and productivity mathematical models during turning of Inconel 718 superalloy (35 HRC) with SiC Whisker ceramic tool at various cutting parameters (depth of cut, feed rate, cutting speed and radius nose). A small central composite design (SCCD) including 16 basics runs replicated three times (48 runs), was adopted and graphically evaluated using Fraction of design space (FDS) graph, completed by a statistical analysis of variance (ANOVA). Mathematical models for surface roughness and productivity were developed and normality was improved using the Box-Cox transformation. Results show that surface roughness criterion Ra was mainly influenced by cutting speed, radius nose and feed rate, and that the depth of cut had major effect on productivity. Finally, ranges of optimized cutting conditions were proposed for serial industrial production. Industrial benefit was illustrated in terms of high surface quality accompanied with high productivity. Indeed, results show that the use of optimal cutting condition had an industrial benefit to 46.9 % as an improvement in surface quality Ra and 160.54 % in productivity MRR.
Nickel based super alloys are excellent for several applications and mainly in structural components submitted to high temperatures owing to their high strength to weight ratio, good corrosion resistance and metallurgical stability such as in cases of jet engine and gas turbine components. The current work presents the experimental investigations of the cutting parameters effects (cutting speed, depth of cut and feed rate) on the surface roughness, cutting force components, productivity and power consumption during dry conditions in straight turning using coated carbide tool. The mathematical models for output parameters have been developed using Box-Behnken design with 15 runs and Box-Cox transformation was used for improving normality. The results of the analysis have shown that the surface finish was statistically sensitive to the feed rate and cutting speed with the contribution of 43.58% and 23.85% respectively, while depth of cut had the greatest effect on the evolution of cutting force components with the contribution of 79.87% for feed force, 66.92% for radial force and 66.26% for tangential force. Multi-objective optimization procedure allowed minimizing roughness Ra, cutting forces and power consumption and maximizing material removal rate using desirability approach.