On the application of response surface methodology for predicting and optimizing surface roughness and cutting forces in hard turning by PVD coated insert

Abstract

This paper focuses on the exploitation of the response surface methodology (RSM) to determine optimum cutting conditions leading to minimum surface roughness and cutting force components. The technique of RSM helps to create an efficient statistical model for studying the evolution of surface roughness and cutting forces according to cutting parameters: cutting speed, feed rate and depth of cut. For this purpose, turning tests of hardened steel alloy (AISI 4140) (56 HRC) were carried out using PVD -coated ceramic insert under different cutting conditions. The equations of surface roughness and cutting forces were achieved by using the experimental data and the technique of the analysis of variance (ANOVA). The obtained results are presented in terms of mean values and confidence levels. It is shown that feed rate and depth of cut are the most influential factors on surface roughness and cutting forces, respectively. In addition, it is underlined that the surface roughness is mainly related to the cutting speed, whereas depth of cut has the greatest effect on the evolution of cutting forces. The optimal machining parameters obtained in this study represent reductions about 6.88%, 3.65%, 19.05% in cutting force components (Fa, Fr, Ft), respectively. The latters are compared with the results of initial cutting parameters for machining AISI 4140 steel in the hard turning process.

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

“…A full quadratic model was used to fit the experimental data and identify the relevant model terms using statistical software (Design Expert 9 and JMP Pro 10 software). As it is indicated by Hessainia et al [22] and Zahia et al [23], a quadratic model, which also includes the linear model, can be described as:…”

“…RSM can represent the direct and interactive effects of process parameters through the analysis of variance (ANOVA). Moreover, this approach applied in the present work is considered as a procedure to identify a relationship between independent input process parameters and output data (process response), which includes commonly six steps as it is indicated by Gaitonde et al [21] and Tebassi et al [20]: (1) define the independent input variables and the desired output responses, (2) adopt an experimental design plan, (3) perform regression analysis with the required model of RSM as found by Hessainia et al [22] and Zahia et al [23] as shown in Eq. (1).…”

Estimation and optimization of flank wear and tool lifespan in finish turning of AISI 304 stainless steel using desirability function approach

“…Xi reveals the coded variables that correspond to the studied machining parameters such as cutting speed (Vc), feed rate (f) and cutting time (t), and ε is a random experimental error. The analysis of variance (ANOVA) has been applied to check the adequacy of the developed machinability models (Bouzid et al, 2015;Berkani et al 2015;Zahia et al 2015;Keblouti et al 2017). The ANOVA table consists of sum of squares and degrees of freedom.…”

Statistical regression modeling and machinability study of hardened AISI 52100 steel using cemented carbide insert

“…Similarly, the significant effect on cutting force components are obtained to be depth of cut; workpiece hardness and feed rate respectively. Zahia et al (2015) studied on hard turning of AISI 4140 (56 HRC) steel using PVD coated ceramic insert. Feed rate and depth of cut are found to be the most significant parameters affecting surface roughness and cutting forces from analysis of variance (ANOVA) study.…”