“…Similar explanations have been mentioned by Refs. 31,32 regarding the effect of the feed rate f on the cutting force Fz.Figure 3.Evolution of Fz as a function of (a) feed rate, (b) depth of cut, (c) cutting speed, andd) tool nose radius.…”
The present study examines the machining of a cold work tool steel (X210Cr12) using a triple chemical vapor deposition coated carbide tool (Al2O3/TiC/TiCN). The paper is focused on an experimental investigation as well as a modeling and optimization of the working cutting parameters in relationship with the studied material. For that, first, a set of experimental tests were built in order to evaluate the effect of cutting parameters (r, Vc, f, and ap) on the output parameters, namely surface roughness (Ra), cutting force (Fz), insert flank wear (Vb), and 3D roughness distribution. In a second step, a Taguchi L16 (4^3 2^1) design of experiment (DoE) was exploited with the aim to develop a modeling of output working parameters based on the response surface methodology. An optimization of the cutting conditions was performed using the desirability function (DF) approach and the hybrid Taguchi-weighted aggregate sum product assessment method. The desired objective is to obtain optimal cutting regime corresponding to the simultaneous minimization of parameters Ra and Fz, and maximization of material removal rate. The results found show that the factor f influences Ra with 42.55% and that parameter ap affects parameters Fz and Pc with 67.55 and 60.88%, respectively. For the DF and WASPAS methods, the optimal regimes selected is r = 1.6 mm, Vc = 366 m/min, ap = 0.17 mm, f = 0.16 mm/rev and r = 1.6 mm, Vc = 180 m/min, ap = 0.3 mm, and f = 0.08 mm/rev, respectively. The proposed work concerns all mechanical manufacturing companies, as it provides the necessary information on the optimal working conditions of the tool/material pair.
“…Similar explanations have been mentioned by Refs. 31,32 regarding the effect of the feed rate f on the cutting force Fz.Figure 3.Evolution of Fz as a function of (a) feed rate, (b) depth of cut, (c) cutting speed, andd) tool nose radius.…”
The present study examines the machining of a cold work tool steel (X210Cr12) using a triple chemical vapor deposition coated carbide tool (Al2O3/TiC/TiCN). The paper is focused on an experimental investigation as well as a modeling and optimization of the working cutting parameters in relationship with the studied material. For that, first, a set of experimental tests were built in order to evaluate the effect of cutting parameters (r, Vc, f, and ap) on the output parameters, namely surface roughness (Ra), cutting force (Fz), insert flank wear (Vb), and 3D roughness distribution. In a second step, a Taguchi L16 (4^3 2^1) design of experiment (DoE) was exploited with the aim to develop a modeling of output working parameters based on the response surface methodology. An optimization of the cutting conditions was performed using the desirability function (DF) approach and the hybrid Taguchi-weighted aggregate sum product assessment method. The desired objective is to obtain optimal cutting regime corresponding to the simultaneous minimization of parameters Ra and Fz, and maximization of material removal rate. The results found show that the factor f influences Ra with 42.55% and that parameter ap affects parameters Fz and Pc with 67.55 and 60.88%, respectively. For the DF and WASPAS methods, the optimal regimes selected is r = 1.6 mm, Vc = 366 m/min, ap = 0.17 mm, f = 0.16 mm/rev and r = 1.6 mm, Vc = 180 m/min, ap = 0.3 mm, and f = 0.08 mm/rev, respectively. The proposed work concerns all mechanical manufacturing companies, as it provides the necessary information on the optimal working conditions of the tool/material pair.
“…In order to address singleobjective and multi-objective problems, four methods were chosen in this study (SA, GA, GRA, and COPRAS). These methods have been investigated extensively in the machining of other materials [18]. When machining a 2.25 Cr1Mo0.25V high-speed steel, Zhang et al optimized the cutting conditions using the DF, GA, and SA methods [19].…”
This paper focuses on a comparative study of the machinability of two semi-crystalline polymers (POM-C) and (PA-6), during dry turning operations. The aim is to experimentally examine the impact of cutting parameters, namely cutting speed, feed, and depth of cut on surface roughness, cutting force, cutting power, and material removal rate. A series of experiments according to a Taguchi L18 plan was implemented. The ANOVA analysis revealed that the type of material a substantial effect on surface roughness, followed by feed, whereas cutting force and cutting power are more affected by depth of cut. Linear regression models with interactions proved effective in predicting the studied responses, and single-objective optimization using SA and GA methods were applied to optimize each response. The GRA and COPRAS methods coupled with weighting methods CRITIC, ROC, SWARA, and Entropy are used for multi-objective optimization of the considered responses. The results showed that the combination of the COPRAS method with the SWARA method provides a better compromise, which is of crucial interest for researchers in the field of optimization in polymer material machining.
“…The longest tool life was achieved with the new tool. Chihaoui et al 15 followed the progression of CBN flank wear ( Vb ) for three different cutting speeds during dry turning of gray cast iron. For permissible flank wear [VB] = 0.3 mm, the cutting tool life found for the three speeds are T 450 = 41.5, T 600 = 18, and T 750 = 7 min.…”
Section: Introductionmentioning
confidence: 99%
“…Several researchers have applied this optimization method to the machining of different types of materials used in the manufacture of mechanical parts in different processes (turning, milling, drilling, EDM, etc.). The studies by Chihaoui et al 15 and Laouissi et al 11 applied (DF) in turning of EN GJL-250 cast iron, Luan et al 20 in milling of HT-250 cast iron, on the other hand, El-Mahalawy et al 21 in EDM machining of ASTM A536 cast iron. Also, Zerti et al, 22 Labidi et al, 23 and Kumar et al 24 used this method to obtain an optimal cutting regime when machining AISI D3 steel.…”
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.
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