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This paper aims to conduct an economic and environmental machining procedure (dry condition) during face milling of the Polyoxymethylene Co-polymer. In this context, an experimental study based on Taguchi’s design was conducted to develop empirical models using Response Surface Methodology (RSM) on one hand and Support Vector Machine (SVM) for regression on the other hand. The ANalysis of VAriance (ANOVA) is conducted to determine the contribution and the significance of each cutting parameter on the surface quality and productivity (MRR). The obtained models are being compared to determine the most efficient approach. The last part is to find the optimum cutting combination by using Genetic Algorithm (GA) optimization based on SVM models, whether to minimize surface roughness Ra, or for composite objective to improve the quality and to increase the productivity. The results show that feed per tooth (fz) is the most affecting parameter on Ra followed by depth of cut (ap) and then the cutting velocity (Vc). SVM was more robust than RSM with less deviation error. [ABSTRACT FROM AUTHOR]

Nowadays, the relationships linking the cutting conditions to the different technological parameters are becoming a major industrial objective. The present work deals with some machinability investigations involving surface roughness and cutting force in finish turning of AISI D3-hardened steel using carbide, ceramic and coated ceramic inserts. The combined effects of the cutting parameters represented by the cutting depth (ap), the cutting tool (Tool), the cutting speed (Vc), and the feed rate (f) on the output parameters illustrated by both the surface roughness (Ra) and the cutting force (Fy) are investigated using the analysis of variance (ANOVA). The modelling of surface roughness and cutting force is carried out using both Response Surface Methodology (RSM) and Artificial Neural Network (ANN). In order to determine the most efficient technique, the models developed are compared in terms of the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Model Predictive Error (MPE). Furthermore, a multi-objective optimization using the Desirability Function Analysis (DFA) has been performed. The obtained results show that the ANN models (For Ra: R²=93%, RMSE=0.02%, MPE=4.99% and for Fy: R²=94%, RMSE=2.52%, MPE=3.11%) performed better than those developed using the RSM approach (For Ra: R²=90.4%, RMSE=0.051%, MPE=18.21% and for Fy: R²=79%, RMSE=31.17%, MPE=55.92%). As a consequence, the achieved ANN models for (Ra) and (Fy) are exploited as objective function for the response optimization applying the DFA technique. The optimum level of the input parameters for the combined desirability is finally identified as ap1–Tool3–Vc3–f1 for both (Ra) and (Fy) with a maximum error of 2.94%.

High speed machining (HSM) is an attractive process for numerous applications due to its potential to increase production rates, reduce lead times, lower costs, and enhance part quality. In this study, high-speed turning operations on AISI D2 steel using a coated carbide cutting tool under dry conditions were conducted. The cutting parameters examined in this investigation were Vc, f , and ap, while the outputs measured were surface roughness (Ra), cutting temperature (T), and flank wear (VB). To obtain reliable and accurate results, a Taguchi L27 orthogonal array for the 27 experimental runs was employed as well as analysis of variance (ANOVA), response surface methodology (RSM), and artificial neural network (ANN) to develop a constitutive relationship between prediction responses and the cutting parameters. The ANOVA results showed that Vc had a significant effect on T (36.81%) and VB (27.58%), while f had a considerable influence on Ra (24.21%). Additionally, nonlinear prediction models were created for each measured output and their accuracy was evaluated using three statistical indices: coefficient of determination (R 2), mean absolute percentage error (MAPE), and root mean square error (RMSE). Finally, multi-objective optimization was successfully carried out using the desirability function (DF) approach to propose an optimal set of cutting parameters that simultaneously minimized Ra, T , and VB. The optimized cutting parameters were Vc = 477.28 m/min, f = 0.08 rev/min, and ap = 0.8 mm, resulting in Ra = 1.23 μ m, T = 1 2 9. 9 ∘ C, and VB = 0.049 mm. [ABSTRACT FROM AUTHOR]

The paper has a dual purpose: firstly, to examine the influence of various cutting conditions (cutting speed Vc, feed f, depth of cut ap, tool nose radius rɛ, and cutting edge angle Xr) on the quality of machined parts (Ra), tangential force (FZ) and cutting power (Pc) during the turning process of polyoxymethylene POM-C. Two carbide inserts, SPMR 120304 and SPMR 120308, were used for the three-dimensional cutting operations. Secondly, the goal is to identify optimal cutting conditions that maximize material removal rate (MRR) while minimizing three output parameters (Ra, FZ, and Pc). The study employed analysis of variance (ANOVA) to assess the significance of the input parameters on the desired outcomes and utilized an artificial neural network (ANN) to create mathematical models. The K-fold Cross-Validation approach was deemed suitable due to its efficiency in requiring fewer experiments. To optimize the cutting conditions, a new metaheuristic optimization algorithm called Multi-Objective Artificial Hummingbird Algorithm (MOAHA) was selected. ANOVA analysis reveals that factors f and rɛ contribute 58.05% and 32.25%, respectively, to the response Ra. Classical parameters (Vc,f, and ap) also impact mechanical cutting actions (FZ and Pc). The MOAHA algorithm, coupled with four ANN models, optimized the five cutting conditions, resulting in optimal values Vc = 250 m/min, f = 0.08 mm/rev, ap = 1.3 mm, rɛ = 0.8 mm, and Xr = 75°. Under these conditions, responses are: Ra = 0.6 μm, FZ = 21.51 N,Pc = 60.24 W, and MRR = 26.38 cm3/min. The ANN-MOAHA coupling provides an excellent, simple, and fast computer tool for multi-objective optimization. [ABSTRACT FROM AUTHOR]

This study focuses on the performance evaluation of CBN and ceramic tools in dry machining of gray cast iron EN GJL-350. The machining factors taken into account during turning are: cutting speed (Vc), feed rate (f), depth of cut (ap), and cutting tool material (CBN, white ceramic, mixed ceramic, and silicon nitride). The first part of this investigation concerns the evaluation of the four cutting materials performance used in terms of tool wear evolutions, 2D and 3D surface roughness and cutting forces variation according to working parameters. The second part exposes the results according to L32 Taguchi design of experiment. Statistical treatment by ANOVA allowed to quantify the impact of the input factors on the performance parameters, namely the surface roughness (Ra), the cutting force (Fz), the cutting power (Pc), and the specific cutting energy (Ecs). The response surface methodology (RSM), and the artificial neural network (ANN) approach were adopted to develop mathematical models for predicting the different output parameters. The results of the two methods were compared and discussed. A multi-criteria optimization was performed using the desirability function (DF) approach. The genetic algorithm (GA) was also applied to find pareto fronts. The results found show that CBN is the most efficient material in terms of lower tool wear, surface roughness and cutting forces. The DF method allowed to find an optimal combination (Vc = 660 m/min, f = 0.13 mm/rev, ap = 0.232 mm, and the CBN material) leading to a compromise between the minimization of (Ra, Fz, Pc, and Ecs) and the maximization of (MRR). The Pareto fronts found by the (GA) method make it possible to propose a multitude of solutions according to the desired objectives. [ABSTRACT FROM AUTHOR]

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.