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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 the current paper, cutting parameters during turning of AISI 304 Austenitic Stainless Steel are studied and optimized using Response Surface Methodology (RSM) and the desirability approach. The cutting tool inserts used in this work were the CVD coated carbide. The cutting speed (vc), the feed rate (f) and the depth of cut (ap) were the main machining parameters considered in this study. The effects of these parameters on the surface roughness (Ra), cutting force (Fc), the specific cutting force (Kc), cutting power (Pc) and the Material Removal Rate (MRR) were analyzed by ANOVA analysis.The results showed that f is the most important parameter that influences Ra with a contribution of 89.69 %, while ap was identified as the most significant parameter (46.46%) influence the Fc followed by f (39.04%). Kc is more influenced by f (38.47%) followed by ap (16.43%) and Vc (7.89%). However, Pc is more influenced by Vc (39.32%) followed by ap (27.50%) and f (23.18%).The Quadratic mathematical models, obtained by the RSM, presenting the evolution of Ra, Fc, Kc and Pc based on (vc, f, and ap) were presented. A comparison between experimental and predicted values presents good agreements with the models found.Optimization of the machining parameters to achieve the maximum MRR and better Ra was carried out by a desirability function. The results showed that the optimal parameters for maximal MRR and best Ra were found as (vc = 350 m/min, f = 0.088 mm/rev, and ap = 0.9 mm).

A finite element based numerical model to simulate orthogonal machining process and associated burr formation process has been developed in the presented work. To incorporate simultaneous effects of mechanical and thermal loadings in high speed machining processes, Johnson and Cook`s thermo-visco-plastic flow stress model has been adopted in the conceived numerical model. A coupled damage-fracture energy approach has been used to observe damage evolution in workpiece and to serve as chip separation criterion. Simulation results concerning chip morphology, nodal temperatures, cutting forces and end (exit) burr have been recorded. Model has been validated by comparing chip morphology and cutting force results with experimental findings in the published literature. Effects of cutting edge geometries [Hone and Chamfer (T-land)] on burr formation have been investigated thoroughly and discussed in length. To propose optimum tool edge geometries for reduced burr formation in machining of an aerospace grade aluminum alloy AA2024, numerical analyses considering multiple combinations of cutting speed (two variations), feed (two variations) and tool edge geometries [Hone edge (two variations), Chamfer edge (four variations)] have been performed. For chamfer cutting edge, the “chamfer length” has been identified as the most influential macro geometrical parameter in enhancing the burr formation. Conversely, “chamfer angle” variation has been found least effecting the burr generation phenomenon.

Broaching operations require stiff machine tools that have to withstand high cutting forces. This work aims to develop a methodology to predict the macroscopic cutting forces on a real, internal broaching operation comprising a large number of teeth. The macroscopic forces are estimated, based on the addition of local forces that are applied to each section and are simultaneously in contact with the broach. These local forces are calculated using a model of specific cutting pressure, depending on the rise per tooth. This study uses two methods to identify this specific cutting pressure model, that is, a direct approach based on orthogonal cutting tests and an inverse approach based on an instrumented broaching operation. It is shown that the direct method is effective in identifying a specific cutting pressure model and enables the prediction of macroscopic forces. Moreover, the direct approach provides more comprehensive results in terms of radial forces.

Single-point incremental forming (SPIF) is a rising technology used shaping sheet-metals. The emergence of this manufacturing process is due to its capability to produce parts with complex shape at lower cost. The present contribution is focused on the presentation of a new designed rolling ball forming tool that can improve (SPIF) operations. For that, the effects of process working parameters on a set of process qualification criteria when forming of AA1050 aluminum alloy sheets is presented. Deep analyses treating impacts of tool step down, tool rolling ball diameter, and tool feed rate on the process responses such as forming axial forces, surface roughness, and sheet thinning are made. The analysis is based on statistical methodologies which made it possible to establish, using the multiple regression method, predictive analytical models for the different responses.

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The present study aims at investigating the influence of the different machining parameters represented by the cutting speed (Vc), the depth of cut (ap), and the feed rate (f) on the output performance parameters expressed through the surface roughness, the cutting force and power, and the material removal rate (i.e., Ra, Fz, Pc, and MRR) during dry hard turning operation of martensitic stainless steel (AISI 420) treated at 59HRC. The machining tests were carried out using the coated mixed ceramic insert (CC6050) according to the Taguchi design (L25). The analysis of the variance (ANOVA) and the Pareto chart analysis led to quantifying the influence of the (Vc, ap, and f) on the output parameters. The response surface methodology (RSM) and the artificial neural networks (ANN) approaches were applied and compared for output parameters modeling. Attempt was further made to optimize the machining parameters using the desirability function (DF). Four objectives were considered including the maximization of the quality and the productivity (through minimizing Ra and maximizing MRR), and reducing the energy consumption over minimizing both (Fz) and (Pc). The results indicated that (Ra) is strongly influenced by the feed rate (in the order of 80.71%), while the depth of cut seems to be the property having the most influence on the cutting force (65.31%), the cutting power (37.56%), and the material removal rate (36.45%). Furthermore, ANN and RSM models were found to predict well experimental results with the former showing higher accuracy. The machining of AISI 420 (59 HRC) steel with coated ceramic led to achieving a quality surface comparable to that found in grinding (i.e., Ra

The final quality of machining is directly a function of the type of machine used. The geometrical and micro quality geometrical of finished surface are one of the principal goals of machining. During the operation of turning, in particular, the elastic behavior of the pin controls the surface quality machined. To say that the rigidity of the machine must be largest possible is not sufficient. The design of the axes of movement of the machine must take account of the effects static, kinematics, dynamic of the mass. The rigidity and the conditions of maintenance by the stages must be qualified in comparison with the results sought in term of machined surface quality. To characterize the effect of the vibrations of the machine tools on the quality of the machined surfaces a study was undertaken on two different lathes, a conventional turn and a turn with numerical control. The results of roughness show that the machine tool exploits a great role the machined surface quality. The rigidity of the machine and its capacities damping are prevalent factors to have a good surface quality. To this end the choice of a thing rigid and damping tool is essential for any trial run and any industrial machining in series.

The present paper consists of an experimental study to the effect of turning parameters on surface roughness of Cobalt alloy (Stellite 6) and the optimization of machining parameters based on Grey relational analysis. Taguchi’s design of experiments (DOE) is used to carry out the tests. The response surface methodology is successfully applied in the analysis of the effect the turning parameters on surface roughness parameters. Second order mathematical models in terms of machining parameters are developed from experimental results. The experiment is carried out by considering four machining conditions, namely noise radius, cutting depth, cutting speed and feed rate as independent variables and average arithmetic roughness as response variables. It can be seen that the tool noise radius and feed rate are the most influential parameters on the surface roughness. The adequacy of the surface roughness model was established using analysis of variance (ANOVA). An attempt was also made to optimize cutting parameters using a Grey relational analysis to achieve minimum surface roughness and maximum material removal rate.

In this study, artificial neural networks (ANNs) were used to study the effects of machinability on cutting parameters during turning of the cobalt alloy (Stellite 6). Cutting forces with three axes (Fx, Fy and Fz) were predicted by changing the tool tip radius (r), cutting speed (Vc), feed rate (f) and cutting depth (ap) with conventional lubrication. Experimental studies were conducted to obtain training and test data and a feed-forward back-propagation algorithm was used in the networks. The main test parameters are the tool tip radius (r, mm), cutting speed (Vc, m/min), feed rate (f, mm/rev), cutting depth (ap, mm) and cutting forces (Fx, Fy and Fz, N). r, Vc, f and ap were used as input data while Fx, Fy and Fz were used as output data. The mean percentage values of root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD) for Fx, Fy and Fz using the proposed models were obtained around 2 and 4.79%, respectively for training and testing. These results show that ANNs can be used to predict the effects of machinability on cutting parameters when cutting Stellite 6 on turning process. The results highlighted the performance of the studied configuration.

The present study consists in identifying the significant effects of various cutting conditions characterizing the machining of the cobalt-based alloy (Stellite 6). For that, an experimental approach based on experimental design was adopted. Predictive models, concerning the evolutions of arithmetic mean roughness, tangential force, material removal rate, and cutting power, were established. Their R2 coefficients are, respectively, 97.15, 99.60, 99.71, and 98.11%. The obtained results demonstrate that both feed rate and insert nose radius are high; the arithmetic mean roughness is getting high. Also, it can be underlined that both depth of cut and feed rate have an important effect on tangential force evolution. Moreover, results demonstrate that depth of cut has the main effect on the evolution of material removal rate and it is followed by cutting speed and feed rate effects. Analysis of variance was applied to find significant cutting parameters affecting surface roughness, tangential force, material removal rate, and cutting power evolutions. A Pareto approach confirms the results obtained by ANOVA. Moreover, a multi-objective optimization based on the desirability function was adopted. The optimization was conducted according to three approaches, which are “quality optimization,” “productivity optimization,” and “quality-productivity combination.”

The “combined turning-burnishing” (“CoTuB”) process is a new methodology to perform both machining and finishing operations simultaneously. The improved surface integrity and reduction in manufacturing time and cost are the main advantages of this technique. The purpose of this paper is to develop a numerical model for combined cutting and burnishing operation, thus explaining the underlying physical phenomenon. The cutting operation is modelled using a new concept named as the “mixed approach”. It consists of replacing the cutting tool by an equivalent thermo-mechanical loading applied on the external surface of the workpiece. It is possible to simulate the CoTuB process using the mixed approach combined with the burnishing ball. The residual stress from the numerical simulation was compared with the experimental results measured by the X-ray diffraction. The 2D model gave a better prediction of the residual stress in the feed direction than in the burnishing direction, whereas the 3D configuration provided a good prediction of the residual stress in both feed and burnishing directions. The developed numerical models gave qualitatively good results of the residual stress for different conditions of burnishing force and feed rate.

Turning modeling and simulation of different metallic materials using the commercially available Finite Element (FE) softwares is getting prime importance because of saving of time and money in com...

Stainless steel is characterized by a poor machinability due to its high ductility, and low thermal conductivity. The objective of this work is to model the output responses namely; surface roughness (Ra), cutting force (Fc), cutting power (Pc), specific cutting force (Ks) and metal removal rate (MRR), during the face milling of the austenitic stainless steel X2CrNi18-9 with coated carbide inserts (GC4040). For so doing, response surface methodology (RMS) is used. A full factorial design (L27) is selected for the experiments and the ANOVA is used in order to evaluate the influence of the cutting parameters namely; cutting speed (Vc), feed per tooth, and depth of cut (ap) on the out-put responses. The optimization is carried out, by using the desirability function (DF), according to three cases; minimizing (Ra), maximizing (MRR), and compromising between (Ra) and (MRR). The results reveal that both of (Ra) and (Ks) are mostly influenced by (fz). Both of (Fc) and (Pc) are found considerably affected by (ap), while (MRR) is influenced by both of (fz) and (ap). The optimum cutting conditions corresponding to the third case are: (Vc = 222 m/min, fz = 0.2 mm/tooth and ap = 0.9 mm). Furthermore, the poor machinability of the austenitic stainless steel is confirmed by the micro-chips welded on the machined surface. Nevertheless, the values of (Ra) did not exceed 2 μm for the admissible tool wear [VB] of 0.3 mm.

The broaching process is used to generate high quality surfaces in very short times. In order to improve the understanding of the evolution of surface quality in broaching, a sensitivity study of the influence on the surface roughness of several broaching parameters dealing with the tool design, the cutting conditions or the workpiece material was performed. Forces and chip formation have also been analyzed so as to highlight the surface roughness generation. This paper shows how the process parameters (such as cutting speed or lubrication), tool design (rise per tooth, tooth angles, substrate material), and workpiece material affects the surface roughness.

Mechanical properties of the metals and their alloys are influenced by the material grain size at microscale. In the present study, the Johnson-Cook (JC) material model is modified to incorporate the effect of material's grain size along with the plasticity coupled damage model. 2D finite element (FE) simulations of turning process of an aerospace grade aluminium alloy 2024 (AA2024) were performed with different grain sizes using a commercial FE software, ABAQUS/Explicit. FE simulation results were compared with the published experimental data on turning process of AA2024. The proposed modified JC material model successfully simulated the increase in cutting force as a function of grain size refinement.

Manufacturers need to continuously improve productivity and reduce the most disadvantages. In the current work, an experimental study has been carried out in order to evaluate the influence of different cutting parameters on the various machining factors such as surface roughness, cutting force, cutting power, metal removal rate, and tool wear during turning of X210Cr12 steel using a multilayer-coated tungsten carbide insert with various nose radii (r). Tests are designed according to Taguchi’s L18 (21 × 34) orthogonal array. ANOVA has been performed to determine the effect of the cutting conditions, and mathematical models have been developed through response surface methodology (RSM). The results indicate that the feed rate and the tool nose radius are the main affecting factors on surface roughness while both tangential force and cutting power are affected mainly by the depth of cut followed by the feed rate and the nose radius. Other special tests of long term have been established in order to study the wear evolution and consequently to determine the tool life. The results indicate also that minimum quantity lubrication (MQL) leads to an important improvement in terms of the cutting tool life by a gain of 23~40% compared to wet and dry machining. It has been found that the MQL is an interesting way to minimize lubrication cost and protect operator health and the environment while keeping better machining quality.

Broaching is a productive and accurate process generating complex surfaces with high quality. In this process, cutting mechanisms occurring are very specific regarding conventional cutting (small uncut chip thickness, low cutting speeds, long encapsulated chips). However, few research have been conducted on this process and in particular in the topic of simulation. For that, an original Lagrangian model that enables to simulate long cutting time by means of an iterative procedure has been developed. Cutting forces, temperatures evolutions and chip morphology obtained with this model have been compared with results from experimental tests and ALE simulations.

The present study investigates the effects of machining parameters and the tool coating during drilling of a GLARE® hybrid aerospace material on the cutting forces, hole quality and the generation of harmful dust particles. The drilling tests were conducted using twist drills with diamond-like carbon (DLC), Cristall coating and without coating. Obtained results indicate that feed, in contrast to spindle speed, has an important effect on thrust force and torque. In addition, when DLC coated tools are used less surface deviation was detected in comparison with uncoated and Cristall coated drills. Moreover, dust measurement in terms of total number of particles, harmful particles and mass particles generated during drilling of GLARE® laminates was investigated. The results reveal that, the number of harmful particles in the air was found to be dependent on machining parameters. In fact, a lower spindle speed promotes the reduction of the number of harmful particles in the air by 50% compared to that observed at higher spindle speed.

Aerospace aluminum alloys have gained the prime significance due to their excellent machining characteristics. Numerous experimental and numerical studies have been conducted to establish the optimum cutting parameters of these alloys. In the numerical cutting models, the authenticity of computational results is suspected particularly because of the complex interaction at tool–chip interface, which involves a high material strain rate and thermal processes. The fidelity of cutting simulation results is appraised by a parametric sensitivity analysis and actual experimentation. In this research, the orthogonal turning of AA2024-T351 aluminum is simulated in Abaqus/Explicit by using a thermoviscoplastic damage model and Coulomb friction model for the contact interfaces. A parametric sensitivity analysis is performed to comprehend the chip morphology, tool–chip interface temperature, reaction force, and strain. Different simulations are performed with varied cutting speeds (200, 400, 600, and 800 m/mi...

International audience; The present contribution deals with the study of the effects of cutting speed, feed rate and depth of cut on the performance of machining which traditionally named “machinability”. The focus is made on the effect of the pre-cited cutting parameters on the evolution of surface roughness and cutting force components during hard turning of AISI D3 cold work tool steel with CC6050 and CC650 ceramic inserts. Also, for both ceramics a comparison of their wear evolution with time and its impact on the surface equality was proposed. The planning of experiments was based on Taguchi’s L16 orthogonal array. The analysis of variance (ANOVA), the signal-to-noise ratio and response surface methodology (RSM) were adopted. Consequently, the validity of proposed linear regression model was checked and the most important parameter affecting the surface roughness and cutting force components were determined. Furthermore, in order to determine the levels of the cutting regime that lead to minimum surface roughness and minimum machining force the relationship between cutting factors was analyzed. The results revealed that the surface quality obtained with the coated CC6050 ceramic insert is 1.6 times better than the one obtained with uncoated CC650 ceramic insert. However, the uncoated ceramic insert was useful in reducing the machining force.

Copper/diamond (Cu/D) composites are known for their applications in thermal management systems. This paper investigates the effect of interfacial thermal resistance (TR) upon the effective thermal conductivity of Cu/D composites through experimental and numerical means. The composite samples were made using uncoated, Cu-coated, and Cr-coated diamond particles. The transient plane source method was used to measure the thermal conductivity of the composite samples, while the micrographs of the specimens were used to develop the finite element models. Together with the experimental and numerical results, the interfacial TR was identified in each sample. Although Hasselman–Johnson model calculated the conductivity values with significant error, the trend shown by experimental results is still followed. The finite element model, however, led to an error of less than 1%. The numerical analysis showed that the TR depends not only upon the diamond volume fraction but also upon the coating material. Finally, it has been demonstrated that numerical simulation may be employed to reveal the appropriate combinations of diamond fraction and the coating material in order to attain the desired level of effective thermal conductivity. Keywords: Thermal conductivity, Interfacial thermal resistance, Numerical simulation, Copper/diamond composite, Thermal conductance, Cr-coated diamond

Austenitic stainless steel (AISI 316L ASS) is known as a very difficult material to cut due to its high toughness, work hardening combined with built-up-edge (BUE) formation and also poor thermal conductivity. In order to improve its machinability, it seems important to carry out experimentation helping to study effects of cutting parameters on process responses. For that both Taguchi and TOPSIS approaches were applied to determine an optimal combination of cutting parameters during dry turning of AISI 316L ASS. Cutting speed (Vc), feed (f), cutting depth (ap) and cutting time (tc) were selected as four input parameters. Flank wear (VB), tangential cutting force (Fz), surface roughness (Ra) and material removal rate (MRR) were considered as the major process responses. Nine cutting tests were carried out based on Taguchi’s L9 orthogonal array. Thus, in order to distinguish the greater significant cutting parameter, Analysis of variance (ANOVA) was applied. Ultimately, in the case of Taguchi approach results show optimal combinations in terms of (Vc, f, ap and tc) for attaining minimum VB, Fz and Ra and also reaching maximization of MRR. In addition, TOPSIS approach was exploited yielding to results that indicate optimal combination of cutting parameters for achieving simultaneously minimum VB, Fz and Ra and maximum MRR.

Post-machining part distortions are commonly observed in thin-walled machined structures. This problem is intensified if the machined part is cellular, curved in profile, and complex in design. Different customized techniques are executed to overcome this issue and to attain the desired dimensional accuracy. This problem instigates mainly due to redistribution of material-induced residual stress subsequent to machining operations. Research work presented here deals with numerical and experimental investigations of part distortions encountered in thin-walled machined structures. Computational analysis and manufacturing simulations of cutting operations are executed in ANSYS® software. Computational work is validated through actual experimentation on a computerized numerical controlled machine with optimum cutting parameters. Coordinate measuring machine (CMM) geometric inspection is carried out to determine the attained machining accuracy. Material-induced residual stress is developed through sequential coupled field thermo-mechanical analysis. Large-scale cutting simulations are performed in an optimized sequence by employing element birth–death feature. Simulation predicted distortions and CMM statistics is evaluated, and it was found that both corroborate with each other. This validates the approach adopted to predict part distortions.

Johnson-Cook constitutive model is still the most used model in metal cutting simulation, although several drawbacks reported in the literature. A high number of Johnson-Cook model parameters can be found in the literature for the same work material. One question that may arise is “What is the most suitable set of Johnson-Cook model parameters for a given material?”. The present paper puts in evidence some issues related with the selection of these parameters from the literature. In this contribution, two sets of Johnson-Cook model parameters for Ti-6A-4 V are evaluated, using three types of metal cutting models. These models are based on three different formulations: Lagrangian, Arbitrary Eulerian-Lagrangian (ALE) and Couple Lagrangian-Eulerian (CEL). This evaluation is based on the comparison between measured and predicted chip geometry, chip compression ratio, forces, plastic deformation and temperature distributions.

For better surface quality and production efficiency in mechanical manufacturing, operators need to make a good choice of cutting parameters such as depth of cut, cutting speed, feed rate and also the cooling condition that affects not only product quality, but also respects the ecological aspect and preserves environment. In the current work, an experimental study has been carried out in order to evaluate the influence of cutting parameters on surface roughness, when turning of X210CR12 steel using multilayer-coated carbide insert with various nose radius. The ANOVA analysis has been performed to determine the effect of cutting conditions on Ra. The results have been analyzed using S/N ratios, mean effect graphs, and 3D response surface plots. The results indicate that the cutting insert nose radius and the feed rate are the mainly affecting factors on surface roughness. Confirmatory experiments have been established after Taguchi’s optimization. It has been found that the MQL is an interesting way to minimize lubricant quantity, protect operator health and environment with keeping better machining quality.

International audience; The present work concerns an experimental study dealing with cutting parameters’ effects on the surface roughness, cutting force, cutting power, and productivity during turning of the polyoxymethylene (POM C) polymer. For that, a cutting tool made of cemented carbide was used. The work is divided into three steps. The first one deals with unifactorial tests, where the evolution of the machining parameters (roughness criteria, cutting force components, and cutting power) is investigated by varying cutting speed, feed rater, and depth of cut. The second part concerns the modeling of the output parameters: arithmetic roughness, cutting force, cutting speed, and material removal rate by using the results of a full factorial design (L27). The second step concerns the adoption of the two modeling techniques, which are the response surface methodology (RSM) and the artificial neural network (ANN). The obtained results related to two both techniques are compared in order to discern the most efficient one. The last step of the present research work concerns the multi-objective optimization using the desirability function (DF). The optimization was carried out according to three approaches, which are the “quality optimization,” “productivity optimization,” and the combination between the quality and productivity.

International audience; This paper is an attempt to compare artificial neural networks and response surface methodology for modeling surface roughness and cutting force in terms of better coefficient of determination (R2), lower root mean square error (RMSE) and model predictive error (MPE). Models were developed based on three-level Box-Behnken design (BBD) of experiments with 15 experimental runs composed of three center points, conducted on Inconel 718 work material using coated carbide insert with cutting speed, feed rate and depth of cut as the process parameters under dry environment. Results show that the artificial neural network (ANN) compared with RSM is a better reliable and accurate approach for predicting and detecting the non-linearity of surface roughness and cutting force mathematical models in terms of correlation and errors. Indeed, the ANN prediction model provides a maximal benefit in terms of precision of 10.1% for cutting force (Fv) and 24.38% for surface roughness (Ra) compared with the RSM prediction model.

International audience; The present paper focuses on the determination of the optimum cutting conditions leading to minimum surface roughness as well as cutting force, cutting power and maximum productivity, in the case of the turning of the Polyoxymethylene polymer POM C using cemented carbide cutting tool. The optimization is based on the response surface methodology, RSM, (desirability function approach). Furthermore, the analysis of variance (ANOVA) is exploited to establish the statistical significance of the cutting parameters on different technological ones studied. The results revealed that the surface roughness is strongly influenced by feed rate with a large contribution, followed by cutting depth, whereas, the cutting speed has no influence. Regarding cutting force, it is found that depth of cut and feed rate are the most significant terms. The RSM allowed the optimization of the cutting conditions for minimal surface roughness, cutting force, cutting power and maximal material removal rate.

International audience; 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.

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.

For qualitative prediction of chip morphology and quantitative prediction of burr size, 2D and 3D finite element (FE) based turning models have been developed in this paper. Coupled temperature-displacement machining simulations exploiting the capabilities of Abaqus® with a particular industrial turning insert and a newly proposed geometrical version of this insert have been performed. Limitations of 2D models in defining the chip morphologies and surface topologies have been discussed. The phenomenological findings on the Poisson burr (Side burr) formation using 3D cutting models have been highlighted. Bespoke geometry of the turning insert has been found helpful in reducing the Poisson burr formation, as it reduces the contact pressures at the edges of tool rake face-workpiece interface. Lower contact pressures serve to decrease the material flow towards workpiece edges (out of plane deformation). In contrast, higher contact pressures at tool rake face-workpiece interface lead to more material flow towards workpiece edges resulting in longer burr. Simulation results of chip morphologies and cutting forces for turning an aluminum alloy A2024-T351 have been compared with the experimental ones. Finally, it has been concluded that the newly proposed geometry of the insert not only decreases the burr but also helpful in lessening the magnitude of tool-workpiece initial impact.

This contribution presents three-dimensional turning operation simulations exploiting the capabilities of finite element (FE) based software Abaqus/Explicit. Coupled temperature-displacement simulations for orthogonal cutting on an aerospace grade aluminum alloy AA2024-T351 with the conceived numerical model have been performed. Numerically computed results of cutting forces have been substantiated with the experimental data. Research work aims to contribute in comprehension of the end-burr formation process in orthogonal cutting. Multi-physical phenomena like crack propagation, evolution of shear zones (positive and negative), pivot-point appearance, thermal softening, etc., effecting burr formation for varying cutting parameters have been highlighted. Additionally, quantitative predictions of end burr lengths with foot type chip formation on the exit edge of the machined workpiece for various cutting parameters including cutting speed, feed rate, and tool rake angles have been made. Onwards, to investigate the influence of each cutting parameter on burr lengths and to find optimum values of cutting parameters statistical analyses using Taguchi&rsquo, s design of experiment (DOE) technique and response surface methodology (RSM) have been performed. Investigations show that feed has a major impact, while cutting speed has the least impact in burr formation. Furthermore, it has been found that the early appearance of the pivot-point on the exit edge of the workpiece surface results in larger end-burr lengths. Results of statistical analyses have been successfully correlated with experimental findings in published literature.

The Table 1 in the original version of this article contained a mistake. Indeed, column 7, “W” must has a value of 3.74 not 15.04.3.74.

This study considers the comparison between the surface roughness criteria (Ra, Rz and Rt) of the wiper inserts with conventional inserts during hard turning of AISI 4140 hardened steel (60 HRC).The planning of experiments was based on Taguchi’s L27 orthogonal array. The response surface methodology (RSM) and analysis of variance (ANOVA) were used to check the validity of quadratic regression model and to determine the significant parameter affecting the surface roughness. The statistical analysis reveals that the feed rate and depth of cut have significant effects in reducing the surface roughness. The optimum machining conditions to produce the best possible surface roughness in the range of this experiment under these experimental conditions searched using desirability function approach for multiple response factors optimization. The results indicate that the surface quality obtained with the wiper ceramic insert significantly improved when compared with conventional ceramic insert is 2.5. Roughness measurements reveal a dependence on CC6050WH tool wear. However, although the wear rises up to the allowable flank wear of value 0.3 mm, roughness Ra did not exceeded 0.9 μm.

This article presents the development of two-dimensional and three-dimensional finite element–based turning models, for better prediction of chip morphology and machined surface topology. Capabilities of a commercial finite element code Abaqus®/Explicit have been exploited to perform coupled temperature–displacement simulations of an aerospace grade aluminum alloy A2024-T351 machining. The findings show that two-dimensional cutting models predict chip morphologies and machined surface textures on a plane section (with unit thickness) passing through the center of workpiece width, and not at the edges. The contribution highlights the importance of three-dimensional machining models for a close corroboration of experimental and numerical results. Three-dimensional cutting simulations show that a small percentage of material volume flows toward workpiece edges (out of plane deformation), augmenting the contact pressures at the edges of tool rake face–workpiece interface. This enhances the burr formation process. Computational results concerning chip morphologies and cutting forces were found in good correlation with experimental ones. In the final part of the article, numerical simulation results with a modified version of a particular turning tool have been discussed. It has been found that the proposed geometry of the tool is helpful in reducing burr formation as well as cutting force amplitude during initial contact of cutting tool with the workpiece material.

This contribution first aims at providing a more detailed analysis of the cutting mechanisms of a normalized carbon steel from a microscopic and metallurgical point of view. SEM and EBSD are used to highlight, in the main intensive deformation zones of the chip, a drastic grain refinement induced by a dynamic recrystallization process. The second part of this study intends to emphasize the consequences of the latter on the workmaterial behaviour. A rheological study based on dynamic compression tests is briefly presented and enables to understand their occurrence as well as their influence on the flow stress of the material. A "metallurgy based" constitutive equation is identified to reach a better description of the flow stress. Finally, this one is implemented in a Finite Element code (Abaqus/Explicit) to assess the potential of this method. It is shown that microstructural evolutions could be relatively well predicted especially when considering the level of recrystallization.