ROUGHNESS VARIATION CAUSED BY GRINDING ERRORS OF CUTTING EDGES IN SIDE MILLING

A numerical model was developed to predict the roughness profile along a line in the feed direction, in contour milling operations with cylindrical milling tools. From the model, average roughness R_a and maximum peak-to-valley roughness R_t were calculated for families of tools defined by 100,000 random combinations of radius values. Radii were selected by means of the Monte Carlo Method. Each family is defined by an average radius and a standard deviation of radius, assuming normal behavior. Histograms, roughness variation intervals, medians and modes, were calculated at different feeds. The model was validated through experimental tests. Effect of standard deviation of radius, number of teeth and tool diameter were studied. Although radius values were randomly selected according to a normal law, roughness values did not follow a normal distribution. At low standard deviations, intervals of roughness values, medians and modes vary only slightly with feed, except at very low feeds where intervals are narrow. On the contrary, at higher standard deviations, median, mode and width of roughness intervals rise as feed increases. Use of a lower number of teeth and higher tool diameter leads to narrower intervals and asymmetrical roughness frequency distributions even at high feeds, with modes near upper reference values.     

The effect of feed rate and of tool nose radius on the roughness of oblique finish turned surfaces

The influence of feed rate and tool nose radius on surface roughness in oblique finish turning of a carbon steel is studied for both sharp and worn cutting tools. A comparison between the experimentally determined values of surface roughness and the theoretical values computed from eqns. (1) and (2) is performed. Linear regression analysis is applied to the experimental data obtained in an attempt to establish the laws relating surface roughness to feed rate and to the expression $\text{s}{}^2\text{R}$, for roughness prediction purposes.     

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.     

Toroidal versus ball nose and flat bottom end mills

This paper compares the surface roughness along and across the feed directions produced by toroidal, ball nose, and flat bottom end mills. The study is conducted numerically and by cutting tests of aluminium. The results show that the toroidal cutter inherits the merits of the other two cutters; it produces small scallops across the feed direction, and low roughness along the feed direction.Nomenclature h scallop height - R _s radius of curvature of surface - inclination angle - 2a _c cross-feed - 2 subtended angle between the point of contact on the tool profile and the surface - R _a surface roughness - e offset distance of insert from tool axes for toroidal cutter - r _c cutter radius - r _i radius of insert for toroidal cutter - f _t feed per tooth - h _u undercut height - y, , intermediate variables     

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.     

Effect of the process parameters on the three-dimensional shape of molten pool during full-penetration laser welding process

Slow tool servo (STS) turning is superior in machining precision and in complicated surface. However, STS turning is a complex process in which many variables can affect the desired results. This paper focuses on surface roughness prediction in lenses STS turning. An exponential model, based on the five main cutting parameters including tool nose radius, feed rate, depth of cut, C-axis speed, and discretization angle, for surface roughness prediction of lenses is developed by means of orthogonal experiment regression analysis. Meanwhile, a prediction model of surface roughness based on least squares support vector machines (LS-SVM) with radial basis function is constructed. Orthogonal experiment swatches are studied, and chaotic particle swarm optimization and leave-one-out cross-validation are applied to determine the model parameters. The comparison of LS-SVM model and exponential model is also carried out. Predictive LS-SVM model is found to be capable of better predictions for surface roughness and has absolute fraction of variance R² of 0.99887, the mean absolute percent error e_M of 8.96 %, and the root mean square error e_R of 10.68 %. The experimental results and prediction of LS-SVM model show that effects of tool nose radius and feed rate are more significant than that of depth of cut on surface roughness of lenses turning.     

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.     

Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning

Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and non-linear. In this work, machining process has been carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness has been measured using surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through feed-forward back-propagation network using Matlab (2009a) software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN has been used confidently. The results obtained conclude that ANN is reliable and accurate for predicting the values. The actual R _a value has been obtained as 1.1999???m and the corresponding predicted surface roughness value is 1.1859???m, which implies greater accuracy.     

Using specially designed high-stiffness burnishing tool to achieve high-quality surface finish

This paper is focused on the process of ball burnishing. The influence of tool stiffness on surface roughness parameters was considered theoretically, while experimental investigation was conducted to establish the influence of initial surface roughness (previous machining) on the effects of ball burnishing as the finishing process. Experimental investigations were conducted over a wide interval of most influential process parameters (burnishing forces, burnishing feed, and number of burnishing passes). The material used in the experiments was aluminum alloy EN AW-6082 (AlMgSi1) T651. Burnishing was performed using a specially designed tool of high stiffness. Statistical analysis of experimental data revealed strong correlation between roughness, R _a, and burnishing force, burnishing feed, and number of passes for the three surfaces, each with different roughness parameters. Particular combinations of process parameters yielded very low surface roughness, R _a, equivalent to polishing. It is worth noting that high surface quality can be achieved with relatively small burnishing forces, which differs from the investigations published so far. Contrary to conventional approaches, which are based on elastic tool systems, the authors propose the burnishing process to be conducted with high-stiffness tools. Further investigation shall be focused on optimization of burnishing process parameters in order to achieve surface finish equivalent to high polish.     

Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods

This study involves modelling of experimental data of surface roughness of Co28Cr6Mo medical alloy machined on a CNC lathe based on cutting parameters (spindle rotational speed, feed rate, depth of cut and tool tip radius). In order to determine critical states of the cutting parameters variance analysis (ANOVA) was applied while optimisation of the parameters affecting the surface roughness was achieved with the Response Surface Methodology (RSM) that is based on the Taguchi orthogonal test design. The validity of the developed models necessary for estimation of the surface roughness values (Ra, Rz), was approximately 92%. It was found that for Ra 38% of the most effective parameters is on the tool tip radius, followed by 33% on the feed rate whereas for Rz tool tip radius occupied 43% with the feed being at 33% rate. To achieve the minimum surface roughness, the optimum values obtained for spindle rpm, feed rate, depth of cut and tool tip radius were respectively, 318 rpm, 0.1 mm/rev, 0.7 mm and 0.8 mm.     

An investigation of surface roughness of burnished AISI 1042 steel

The aim of this study is to analyse the evolution of surface roughness finished by burnishing. Burnishing is done on a surface that was initially turned or turned and then ground.It has been noted that burnishing an AISI 1042 steel offers the best surface quality when using a small feed value. This finishing process improves roughness and introduces compressive residual stresses in the machined surface. So, it can replace grinding in the machining range of the piece.Also, an analytical model has been defined to determine the R_t factor in relation to the feed. Good correlations have been found between the experimental and analytical results.     

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.     

Atomic force microscopy imaging of polycrystalline CuInSe2 thin films

In this study, atomic force microscopy (AFM) imaging has been used to study the structural properties of polycrystalline CuInSe₂ films, which are widely used as absorber materials in thin film solar cell devices. This technique demonstrated an excellent capability for the reproducible imaging of these rough polycrystalline materials. AFM imaging in combination with statistical analysis revealed distinct differences in the structural properties (i.e. grain width and height distributions, root‐mean‐square (RMS) and peak to valley (R_(p–v)) roughness values) as a function of the specific growth technique used and the bulk composition of the films. In the case of Cu‐rich films, prepared by the H₂Se/Ar treatment of Cu/In/Cu alloys, rough surface structures were in general observed. Statistical analysis revealed two distinct distribution of grains in these samples (1.0–2.5 μm and 3–5.5 μm) with large RMS and R_(p–v) roughness values of 380 nm and 2.6 μm, respectively. In‐rich films were characterized by the presence of much smaller, roughly circular clusters with a significant reduction in both the width and height distributions as well as RMS and R_(p–v) roughness values. The most successful growth techniques, in terms of producing homogeneous and dense films, were in the cases of H₂Se/Ar treated metallic InSe/Cu/InSe alloys and the coevaporation of all materials to form CuInSe₂. Both these techniques produced absorber films with very narrow grain width and height distributions as well as small roughness values. It was possible to establish that high efficiency devices are associated with the use of absorber films with narrow width distributions between 0.5 and 2 μm and small RMS (> 300 nm) roughness values. These values are used as a figure of merit in our laboratories to evaluate the structural properties of our CuInSe₂ thin films.     

Sustainability analyses of cutting edge radius on specific cutting energy and surface finish in side milling processes

The aim of this work is to determine the influence of cutting edge radius on the specific cutting energy and surface finish in a mechanical machining process. This was achieved by assessing the direct electrical energy demand during side milling of aluminium AW6082-T6 alloy and AISI 1018 steel in a dry cutting environment using three different cutting tool inserts. The specific energy coefficient was evaluated as an index of the sustainable milling process. The surface finish of the machined parts was also investigated after machining. It was observed that machining with the 48.50-μm cutting edge radius insert resulted in lower specific cutting energy requirements when compared with the 68.50 and 98.72-μm cutting edge radii inserts, respectively. However, as the ratio of the undeformed chip thickness to cutting edge radius is less than 1, the surface roughness increases. The surface roughness values gradually decrease as the ratio of undeformed chip thickness to cutting edge radius (h/r _e) tends to be 1 and at minimum surface roughness values when the ratio of h/r _e equalled to 1. However, the surface roughness values increased as h/r _e becomes higher than 1. This machining strategy further elucidates the black box and trade-offs of ploughing and rubbing characteristics of micro machining and optimization strategy for minimum energy and sustainable manufacture.     

Assessment of flank wear and nose radius wear from workpiece roughness profile in turning operation using machine vision

Although literature on the measurement of flank wear and crater wear in single-point turning tools using machine vision is well documented, the study on the effect of nose radius wear on the roughness profile and dimensional changes of workpiece is less explored. The measurement of flank wear using the 2-D profile of the tool nose region or the roughness profile of the workpiece has also not been attempted in the past. In this work, the nose radius wear of cutting tools and roughness profile of turned parts in a lathe operation were measured using the machine vision method. The flank wear width VB_C in the nose area was determined from the nose radius wear using the tool setup and machining geometry. The nose radius wear was also determined from the roughness profile of the workpiece and used in calculating the flank wear width. Comparison between the maximum flank wear width VB_Cmax determined from the roughness profile and that obtained using a toolmaker’s microscope showed a mean deviation of 5.5%. This result indicates that flank wear can be determined fairly accurately from the workpiece roughness profile if the tool and machining geometry are known.     

On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al₂O₃/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.     

Study of face milling of hardened AISI D3 steel with a special design of carbide tools

This paper studies the impact of a special carbide tool design on the process viability of the face milling of hardened AISI D3 steel (with a hardness of 60 HRC), in terms of surface quality and tool life. Due to the advances in the manufacturing of PVD AlCrN tungsten carbide coated tools, it is possible to use them in the manufacturing of mould and die components. Experimental results show that surface roughness (R_a) values from 0.1 to 0.3 μm can be obtained in the workpiece with an acceptable level of tool life. These outcomes suggest that these tools are suitable for the finishing of hardened steel parts and can compete with other finishing processes. The tool performance is explained after a tool wear characterization, in which two wear zones were distinguished: the region along the cutting edge where the cutting angle (κ) is maximum (κ_max) for a given depth of cut, and the zone where the cutting angle is minimum (κ?=?0) that generates the desired surface. An additional machining test run was made to plot the topography of the surface and to measure dimensional variations. Finally, for the parameters optimal selection, frequency histograms of R_a distribution were obtained establishing the relationship between key milling process parameters (V_c and f_z), surface roughness and tool wear morphology.     

OPTIMIZATION OF THE CUTTING EDGE GEOMETRY OF COATED CARBIDE TOOLS IN DRY TURNING OF STEELS USING A FINITE ELEMENT ANALYSIS

This paper investigates the effects of edge radius of a round-edge coated carbide tool on chip formation, cutting forces, and tool stresses in orthogonal cutting of an alloy steel 42CrMo₄ (AISI 4142H). A comprehensive experimental study by end turning of thin-walled tubes is conducted, using advanced coated tools with well-defined cutting edge radii ranging from 5 to 68 microns. In parallel, 2-D finite element cutting simulations based on Lagrangian thermo-viscoplastic formulation are used to predict the cutting temperatures and tool-stress distributions within the tool coating and substrate. The results obtained from this study provide a fundamental understanding of the cutting mechanics for the coated carbide tool used, and can assist in the optimization of tool edge design for more complex geometries, such as chamfered edge. Specifically, the results obtained from the experiments and simulations of this study demonstrated that finite element analysis can significantly help in optimizing the design of coated cutting tools through the prediction of tool stresses and temperatures, especially within the coating layer.     

Machining of austenitic stainless steels using CVD multi-layer coated cemented carbide tools

This paper presents the results of experimental work in dry turning of austenitic stainless steels (AISI 304 and AISI 316) using CVD multi-layer coated cemented carbide tools. The turning tests were conducted at four different cutting speeds (120, 150, 180 and 210 m/min) while feed rate and depth of cut were kept constant at 0.16 mm/rev and 1 mm, respectively. The cutting tools used were TiC/TiCN/TiN and TiCN/TiC/Al₂O₃ coated cementide carbides. The influences of cutting speed, cutting tool coating top layer and workpiece material were investigated on the machined surface roughness and the cutting forces. The worn parts of the cutting tools were also examined under scanning electron microscope (SEM). The results showed that cutting speed significantly affected the machined surface roughness values. With increasing cutting speed, the surface roughness values decreased until a minimum value is reached beyond which they increased.     

Focused ion beam-shaped microtools for ultra-precision machining of cylindrical components

Focused ion beam (FIB) sputtering is used to shape a variety of cutting tools with dimensions in the 15–100 μm range and cutting edge radii of curvature of 40 nm. The shape of each microtool is controlled to a pre-specified geometry that includes rake and relief features. We demonstrate tools having rectangular, triangular, and other complex-shaped face designs. A double-triangle tip on one tool is unique and demonstrates the versatility of the fabrication process. The FIB technique allows observation of the tool during fabrication, and, thus, reproducible features are generated with sub-micron precision. Tools are made from tungsten carbide, high-speed tool steel, and single crystal diamond. Application of FIB-shaped tools in ultra-precision microgrooving tests shows that the cross-section of a machined groove is an excellent replication of the microtool face. Microgrooves on 40–150 μm pitch are cut into 3 mm diameter polymer rods, for groove arc lengths greater than 12 cm. The surface finish of machined features is also reported; groove roughness (R_a) is typically less than 0.2 μm. Ultra-precision machining of cylindrical substrates is extended to make bound metal microcoils having feature sizes of 20–40 μm.