Comparative assessment of wiper and conventional ceramic tools on surface roughness in hard turning AISI 4140 steel

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.     

Investigations on machined metal surfaces through the stylus type and optical 3D instruments and their mathematical modeling with the help of statistical techniques

The measurement of roughness on machined metal surfaces is of considerable importance to manufacturing industries as the roughness of a surface has a significant influence on its quality and function of products. In this paper, an experimental approach for surface roughness measurement has been based on the comparison of roughness values taken from the stylus and optical type instruments on the machined metal surfaces (turning, grinding and milling) is presented.Following this experimental study, all measured surface roughness parameters have been analyzed by using Statistical Package for Social Science (SPSS 15.0) statistically and mathematical models for the two most important and commonly used roughness parameters R_a and R_z have been developed so that R_a = R_a (F, P, C) and R_z = R_z (F, P, C, M), whereas F expresses feed, P periodicity, C contrast and M the type of material. The statistical results from numerous tests showed that there has been a correlation between the surface roughness and the properties of the surface topography and there have been slight differences among three measurement instruments on machined metal surfaces in this experimental study.     

Prediction of surface roughness of turned surfaces using neural networks

In this study, the prediction of surface roughness heights R_a and R_t of turned surfaces was carried out using neural networks with seven inputs, namely, tool insert grade, workpiece material, tool nose radius, rake angle, depth of cut, spindle rate, and feed rate. Coated carbide, polycrystalline and single crystal diamond inserts were used to conduct 304 turning experiments on a lathe, and surface roughness heights of the turned surfaces were measured. A systematic approach to obtain an optimal network was employed to consider the effects of network architecture and activation functions on the prediction accuracy of the neural network for this application. The reliability of the optimized neural network was further explored by predicting the roughness of surfaces turned on another lathe, and the results proved that the network was equally effective in predicting the R_a and R_t values of the surfaces machined on this lathe as well.     

Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression

In this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R _a) and total (R _t) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R _a and R _t values for each case are measured. Input–output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.     

Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron

This paper focused on optimizing the cutting conditions for the average surface roughness (R_a) obtained in machining of high-alloy white cast iron (Ni-Hard) at two different hardness levels (50 HRC and 62 HRC). Machining experiments were performed at the CNC lathe using ceramic and cubic boron nitride (CBN) cutting tools on Ni-Hard materials. Cutting speed, feed rate and depth of cut were chosen as the cutting parameters. Taguchi L₁₈ orthogonal array was used to design of experiment. Optimal cutting conditions was determined using the signal-to-noise (S/N) ratio which was calculated for R_a according to the “the-smaller-the-better” approach. The effects of the cutting parameters and tool materials on surface roughness were evaluated by the analysis of variance. The statistical analysis indicated that the parameters that have the biggest effect on R_a for Ni-Hard materials with 50 HRC and 62 HRC are the cutting speed and feed rate, respectively. Additionally, the optimum cutting conditions for the materials with 50 HRC and 62 HRC was found at different levels.     

A comparative study on performance of multilayer coated and uncoated carbide inserts when turning AISI D2 steel under dry environment

The present work deals with a comparative study on flank wear, surface roughness, tool life, volume of chip removal and economical feasibility in turning high carbon high chromium AISI D2 steel with multilayer MTCVD coated [TiN/TiCN/Al₂O₃/TiN] and uncoated carbide inserts under dry cutting environment. Higher micro hardness of TiN coated carbide samples (1880 HV) compared to uncoated carbide (1430 HV) is observed and depicts better resistance against abrasion. The low erosion rate was observed in TiN coated insert compared to uncoated carbide. The tool life of TiN coated insert is found to be approximately 30 times higher than the uncoated carbide insert under similar cutting conditions and produced lower surface roughness compared to uncoated carbide insert. The dominant wear mechanism was found to be abrasion and progression of wear was steady using multilayer TiN coated carbide insert. The developed regression model shows high determination coefficient i.e. R² = 0.977 for flank wear and 0.94 for surface roughness and accurately explains the relationship between the responses and the independent variable. The machining cost per part for uncoated carbide insert is found to be 10.5 times higher than the multilayer TiN coated carbide inserts. This indicates 90.5% cost savings using multilayer TiN coated inserts by the adoption of a cutting speed of 200 m/min coupled with a tool feed rate of 0.21 mm/rev and depth of cut of 0.4 mm. Thus, TiN coated carbide tools are capable of reducing machining costs and performs better than uncoated carbide inserts in machining D2 steel.     

Simulation of surface generated during abrasive flow finishing of Al/SiCp-MMC using neural networks

Abrasive flow machining (AFM) is a multivariable finishing process which finds its use in difficult to finish surfaces on difficult to finish materials. Near accurate prediction of generated surface by this process could be very useful for the practicing engineers. Conventionally, regression models are used for such prediction. This paper presents the use of artificial neural networks (ANN) for modeling and simulation of response characteristics during AFM process in finishing of Al/SiC_p metal matrix composites (MMCs) components. A generalized back-propagation neural network with five inputs, four outputs, and one hidden layer is designed. Based upon the experimental data of the effects of AFM process parameters, e.g., abrasive mesh size, number of finishing cycles, extrusion pressure, percentage of abrasive concentration, and media viscosity grade, on performance characteristics, e.g., arithmetic mean value of surface roughness (R _a, micrometers), maximum peak–valley surface roughness height (R _t, micrometers), improvement in R _a (i.e., ΔR _a), and improvement in R _t (i.e., ΔR _t), the networks are trained for finishing of Al/SiC_p-MMC cylindrical components. ANN models are compared with multivariable regression analysis models, and their prediction accuracy is experimentally validated.     

Investigation for optimal parametric combination for achieving better surface finish during turning of Al/SiC-MMC

This paper presents an experimental investigation of the influence of cutting conditions on surface finish during turning of Al/SiC-MMC. In this study, the Taguchi method, a powerful tool for experiment design,is used to optimise cutting parameters for effective turning of Al/SiC-MMC using a fixed rhombic tooling system. An orthogonal L₂₇(3¹³) array is used for 3³ factorial design and analysis of variance (ANOVA) is employed to investigate the influence of cutting speed, feed and depth of cut on the surface roughness height R _a and R _t respectively. The influence of the interaction of cutting speed/feed on the surface roughness height R _a and R _t and the effect of cutting speed on cutting speed/feed two factor cell total interaction for surface roughness height R _a and R _t are analysed through various graphical representations. Taking significant cutting parameters into consideration and using multiple linear-regression, mathematical models relating to surface roughness height R _a and R _t are established to investigate the influence of cutting parameters during turning of Al/SiC-MMC. Confirmation test results established the fact that the mathematical models are appropriate for effectively representing machining performance criteria, e.g. surface roughness heights during turning of Al/SiC-MMC.     

Analytical Surface Roughness Parameters of a Theoretical Profile Consisting of Elliptical Arcs

The closed‐form solutions of surface roughness parameters for a theoretical profile consisting of elliptical arcs are presented. Parabolic and simplified approximation methods are commonly used to estimate the surface roughness parameters for such machined surface profiles. The closed‐form solution presented in this study reveals the range of errors of approximation methods for any elliptical arc size. Using both implicit and parametric methods, the closed‐form solutions of three surface roughness parameters, R _t , R _a , and R _q , were derived. Their dimensionless expressions were also studied and a single chart was developed to present the surface roughness parameters. This research provides a guideline on the use of approximate methods. The error is smaller than 1.6% when the ratio of the feed and major semi‐axis of the elliptical arc is smaller than 0.5. The closed‐form expressions developed in this study can be used for the surface roughness modeling in CAD/CAM simulations.     

Effect of wedge angle on surface roughness in finish turning: analytical and experimental study

Most of the theoretical models for surface roughness in finish turning assume that the work piece surface profile is formed by the rounded tip of the tool nose. The effect of the straight flank section in the tool nose region on the surface roughness is usually neglected. In this work, the straight flank section is taken into account in order to predict the arithmetic average roughness R _a and root-mean-square roughness R _q more accurately. The analytical models for R _a and R _q are developed as a function of three parameters, namely feed rate, nose radius, and wedge angle. These models were verified using digital simulation method. The surface roughness determined using the new three-parameter models were compared with the existing two-parameter models that consider only the feed rate and nose radius. Decreasing wedge angle was found to lower the surface roughness significantly. An experiment was conducted to test the validity of the three-parameter model at different feed rates in real machining operation. The experimental results agreed more closely with the proposed three-parameter models compared to the two-parameter models.     

Modeling and prediction for 3D surface topography in finish turning with conventional and wiper inserts

Wiper insert have the characteristics of achieving an excellent surface finish and improving the productivity in turning processes. Wiper insert can provide twice feed rate while maintaining the comparable surface roughness compared to that provided by the conventional insert. In the present study, surface topographies in finish turning with conventional and wiper inserts are investigated. The key element of this work is that the cutting edge path equation in the cutting tool coordinate system is transformed into the machine tool and workpiece coordinate system by the use of spatial coordinate transformation. Following that a surface topography simulation algorithm based on the cutting edge path equation and cutting parameters is put forward. The output of this work is that both the simulated surface topography and surface roughness profile are good agreement with the experimental results. Both the simulated and the actual machined surface results show that better surface topography is obtained in finish turning with the wiper insert than that with conventional insert. Burnishing effect of the wiper insert leads to half decrease of the Ra and Rz. The actual surface profiles are no longer regular wave shapes due to ploughing effect and side flow existing in the cutting zone. In addition, a surface roughness map has also been developed to optimize the selection of wiper radius and feed rate to satisfy the requirement of surface finishing with higher productivity. From the viewpoint of cutting tool design, the wiper radius with five times larger than tool nose radius can fully come into its role. This provides a novel insight into the design of wiper insert over conventional techniques. Above all, the proposed model gives a better prediction of surface roughness in finish turning process compared to the previous empirical and regression roughness models. The prediction of surface roughness in finish turning with wiper insert is also realized.     

Modeling surface roughness for polishing process based on abrasive cutting and probability theory

The surface roughness is a variable used to describe the quality of polished surface. This article presents a surface roughness model based on abrasive cutting and probability theory, which considers the effects of abrasive grain shape, grit and distribution feature, pressure on surface roughness. The abrasive grain protrusion heights are thought to close to Gaussian distribution, and then the relationship between the indentation depth and the pressure based on Hertz contact theory is obtained. Surface roughness prediction model is established by calculating indentation depth of the abrasive grains on workpiece surface. The maximum surface profile height (R_y) is approximately equal to the maximum indentation depth of the abrasive grain. The arithmetic average surface roughness (R_a) is equal to the average indentation depth of the abrasive grain. The effects of process parameters such as pressure and grit on R_y and R_a were simulated and analyzed in detail.     

Groove wear,built-up edge and surface roughness in turning

The variation of groove wear profile, built up edge adhering to the machined surface and surface roughness have been studied. The correlation between the surface roughness (R_a and R_t), groove wear and built up edge is discussed.     

Surface finish prediction models for precision grinding of silicon

Conventional grinding of silicon substrates results in poor surface quality unless they are machined in ductile mode on expensive ultra-precision machine tools. However, precision grinding can be used to generate massive ductile surfaces on silicon so that the polishing time can be reduced immensely and surface quality improved. However, precision grinding has to be planned with reliability in advance and the process has to be performed with high rates of reproducibility. Therefore, this work reports the empirical models developed for surface parameters R _a, R _max, and R _t with precision grinding parameters, depths of cut, feed rates, and spindle speeds using conventional numerical control machine tools with Box–Behnken design. Second-order models are developed for the surface parameters in relation to the grinding parameters. Analysis of variance is used to show the parameters as well as their interactions that influence the roughness models. The models are capable of navigating the design space. Also, the results show large amounts of ductile streaks at depth of cut of 20?μm, feed rate of 6.25?mm/min, and spindle speed of 70,000?rpm with a 43-nm R _a. Optimization experiments by desirability function generate 37-nm R _a, 400-nm R _max, and 880-nm R _t with massive ductile surfaces.     

Enhancing AFM process productivity through improved fixturing

Abrasive flow machining (AFM) is a non-conventional finishing process that deburrs and polishes by forcing an abrasive laden media across the workpiece surface. The process embraces a wide range of applications from critical aerospace and medical components to high-production volumes of parts. One serious limitation of this process is its low productivity in terms of rate of improvement in surface roughness. Limited efforts have hitherto been directed towards enhancing the productivity of this process with regard to better quality of workpiece surface. This paper discusses improved fixturing as a technique for productivity enhancement in terms of surface roughness (R _a). A rotating centrifugal-force-generating (CFG) rod is used inside the cylindrical workpiece which provides the centrifugal force to the abrasive particles normal to the axis of workpiece. The effect of the key parameters on the performance of process has been studied. The results shows that for a given improvement in R _a value, the processing time can be reduced by as much as 70–80%. It is seen that the significant process parameters are revolutions per minute of CFG rod, extrusion pressure and abrasive mesh size.     

Geometry simulation and evaluation of the surface topography in five-axis ball-end milling

Limited by the factors such as dynamic vibrations, cutting heat, and the use of coolant, it is difficult to measure or evaluate the surface quality in real time. Geometry simulation of the surface topography became the main method used in engineering to estimate and control the quality of the surface machining. This paper proposed a new method for geometry simulation and evaluation of a milled surface. Allowing for the coherency in geometric variations management process, the proposed method is developed based on the skin model of a workpiece. To make the simulated surface topography more realistic, the effects of locating errors, spindle errors, geometrical errors of the machine tool, and cutting tool deflections are included. And a new method is adopted to evaluate the milled surface, in which the roughness of the surface is characterized by the modal coefficients, instead of the R _a , R _z , and R _q values. At the end of this paper, measurements and cutting tests are carried out to validate the proposed method.     

Reducing electrode wear ratio using cryogenic cooling during electrical discharge machining

In this study, cooling effect of copper electrode on the die-sinking of electrical discharge machining of titanium alloy (Ti-6Al-4V) has been carried out. Investigation on the effect of cooling on electrode wear and surface roughness of the workpiece has been carried out. Design of experiment plan for rotatable central composite design of second order with four variables at five levels each has been employed to carry out the investigation. Current intensity (I), pulse on-time (t _on), pulse off-time (t _off), and gap voltage (v) were considered as the machining parameters, while electrode wear and surface roughness are the responses. Analysis of the influence of cooling on the responses has been carried out and presented in this study. It was possible to reduce electrode wear ratio up to 27% by electrode cooling. Surface roughness was also reduced while machining with electrode cooling.     

Improved measure of workpiece surface deterioration during turning using non-contact vision method

The surface finish quality of a machined workpiece is commonly measured using the average roughness parameter, R_a. This parameter, however, is insensitive to the lateral changes undergone by the surface in the feed direction as a consequence of tool wear. In this work, the effectiveness of four methods of workpiece surface analysis, namely autocorrelation, cross-correlation, and two new methods, called lateral material shift (LMS) ratio and profile slope ratio (PSR) analyses are investigated. Dry machining experiments were carried out on 316 stainless steel. Images of tool nose and workpiece profiles were captured using digital camera, and the edges were extracted using sub-pixel edge detection. In the autocorrelation approach, each workpiece profile was correlated with a shifted version of the same profile. In the cross-correlation approach, the workpiece profiles at different stages of machining were correlated with a reference profile generated using the unworn tool edge. In the LMS ratio method, the material shift ratios were determined from each waveform on the workpiece profile at various stages of tool wear, while in the PSR method the slopes at the right and left part of the waveform were compared. Among the four methods, the LMS ratio method produced the best correlation with tool flank wear with the maximum R-squared value of 0.9461, while average roughness R_a showed no correlation at all with both major and nose flank wear.     

Process investigation on meso-scale hard milling of ZrO2 by diamond coated tools

This paper investigates the feasible machining of zirconium oxide (ZrO₂) ceramics, in the hard state, via milling by diamond coated miniature tools (from here on briefly indicated as meso-scale hard milling). The workpiece material is a fully sintered yttria stabilized tetragonal zirconia polycrystalline ceramic (Y-TZP). Diamond coated WC mills, 2 mm in diameter, four flutes and large corner radius (0.5 mm) are chosen as cutting tools, and experiments are conducted on a state-of-the-art micro milling machine centre. The influence of cutting parameters, including axial depth of cut (a_p) and feed per tooth (f_z), on the achievable surface quality is studied by means of a one-factor variation experimental design. Further tests are also conducted to monitor the process performance, including surface roughness, tool wear and machining accuracy, over the machining time. Mirror quality surfaces, with average surface roughness Ra below 80 nm, are obtained on the machined samples; the SEM observations of the surface topography reveal a prevailing ductile cutting appearance. Tool wear initiates with delamination of the diamond coating and progresses with the wear of the WC substrate, with significant effect on the cutting process and its performance. Main applications of this research include three dimensional surface micro structuring and superior surface finishing.     

Measurement of the surface roughness of wood based materials used in furniture manufacture

The objective of this work was to evaluate surface quality of wood based materials used to manufacture furniture units in Singapore. Various type commercially produced composite panels including particleboard, medium density fibreboard (MDF), plywood in addition to ten different solid wood species which are commonly used in furniture production were considered for the experiments. A stylus type profilometer and 3D image analyzer were employed to determine surface roughness of the samples. Medium density fibreboard (MDF) samples resulted in the smoothest surface with an across the sandmark average roughness (R_a) value of 5.07 μm, while corresponding value for plywood specimens was 8.09 μm among the composite panel samples. In the case of solid wood samples, measurements taken along and across the sandmark from the surface of the specimens measured by the stylus type profilometer, balau had the roughest surface with an R_a value of 9.85 μm across the sandmark followed by beech and walnut. Pine specimens along with ash, cherry and nyatoh resulted in relatively smooth surface values. Correlation between measurements taken by two different methods, namely stylus and 3D scanning showed a good agreement with each other. Based on the findings in this work it appears that both methods can be successfully used to evaluate and to get objective numerical values on surface quality of these samples so that such initial data can be used as quality control tool to have more effective further manufacturing steps in furniture production.