International audienceThe objective of this article is to manufacture low-cost, high-quality products with maximum productivity in short time. In this work, four stages are considered: statistical investigation of the experimental results based on ANOVA, modelling based on regression analysis and mono- and multi-objective optimizations. In the first stage, turning experiments were carried out using an orthogonal array (L16) of Taguchi. Effects of cutting parameters on surface roughness and material removal rate were determined using ANOVA and interaction plots. In the second stage, regression analysis was utilized to formulate second-order models of all data gathered in the experimental works; these models could be used to predict responses in turning of X20Cr13 steel with a minor error. In the third stage, responses were used alone in an optimization study as an objective function. To minimize all responses, Taguchi’s signal-to-noise ratio was used. In the fourth stage, responses were optimized simultaneously using grey relational analysis

In this study, an attempt has been made to statistically model the relationship between cutting parameters (speed, feed rate and depth of cut), cutting force components ( Fx, Fy and Fz) and workpiece absolute surface roughness ( Ra). The machining case of a martensitic stainless steel (AISI 420) is considered in a common turning process by means of a chemical vapor deposition–coated carbide tool. A full-factorial design (43) is adopted in order to analyze obtained experimental results via both analysis of variance and response surface methodology techniques. The optimum cutting conditions are achieved using mutually response surface methodology and desirability function approaches while the model adequacy is checked from residual values. The results indicated that the depth of cut is the dominant factor affecting ( Fx: 86%, Fy: 58% and Fz: 81%), whereas feed rate is found to be the utmost factor influencing surface roughness behavior ( Ra: 81%). In addition, a good agreement between the predicted and measured cutting force components and surface roughness was observed. The results are also validated experimentally by determining errors ( Fx: 6.51%, Fy: 4.36%, Fz: 3.59% and Ra: 5.12%). Finally, the ranges for optimal cutting conditions are projected for serial industrial production.

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.

The wear of cutting tools remains a major obstacle. The effects of wear are not only antagonistic at the lifespan and productivity, but also harmful with the surface quality. The present work deals with some machinability studies on flank wear, surface roughness, and lifespan in finish turning of AISI 304 stainless steel using multilayer Ti(C,N)/Al2O3/TiN coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), and combined effects of two cutting parameters, namely cutting speed and feed rate on lifespan (T), are explored employing the analysis of variance (ANOVA). The relationship between the variables and the technological parameters is determined using a quadratic regression model and optimal cutting conditions for each performance level are established through desirability function approach (DFA) optimization. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. In addition, it is indicated that the cutting time is the dominant factor affecting workpiece surface roughness followed by feed rate, while lifespan is influenced by cutting speed. The optimum level of input parameters for composite desirability was found Vc1-f1-t1 for VB, Ra and Vc1-f1 for T, with a maximum percentage of error 6.38%.

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