Optimization of surface roughness in end milling Castamide

Castamide is vulnerable to humidity up to 7%; therefore, it is important to know the effect of processing parameters on Castamide with and without humidity during machining. In this study, obtained quality of surface roughness of Castamide block samples prepared in wet and dry conditions, which is processed by using the same cutting parameters, were compared. Moreover, an artificial neural network (ANN) modeling technique was developed with the results obtained from the experiments. For the training of ANN model, material type, cutting speed, cutting rate, and depth of cutting parameters were used. In this way, average surface roughness values could be estimated without performing actual application for those values. Various experimental results for different material types with cutting parameters were evaluated by different ANN training algorithms. So, it aims to define the average surface roughness with minimum error by using the best reliable ANN training algorithm. Parameters as cutting speed (V _c), feed rate (f), diameter of cutting equipment, and depth of cut (a _p) have been used as the input layers; average surface roughness has been also used as output layer. For testing data, root mean squared error, the fraction of variance (R ²), and mean absolute percentage error were found to be 0.0681%, 0.9999%, and 0.1563%, respectively. With these results, we believe that the ANN can be used for prediction of average surface roughness.     

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.     

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.     

Surface roughness measurement in turning carbon steel AISI 1045 using wiper inserts

Surface roughness of the workpiece is an important parameter in machining technology. Wiper inserts have emerged as a significantly class of cutting tools, which are increasingly being utilized in last years. This study considers the influence of the wiper inserts when compared with conventional inserts on the surface roughness obtained in turning. Experimental studies were carried out for the carbon steel AISI 1045 because of its great application in manufacturing industry. Surface roughness is represented by different amplitude parameters (R_a, R_zD, R_3z, R_q, R_t, R_a/R_q, R_q/R_t, R_a/R_t). With wiper inserts and high feed rate it is possible to obtain machined surfaces with R_a < 0.8 μm (micron). Consequently it is possible to get surface quality in workpiece of mechanics precision without cylindrical grinding operations.     

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.     

An integrated optimization of cutting parameters and tool path generation in ultraprecision raster milling

This paper provides a new methodology for the integrated optimization of cutting parameters and tool path generation (TPG) based on the development of prediction models for surface roughness and machining time in ultraprecision raster milling (UPRM). The proposed methodology simultaneously optimizes the cutting feed rate, the path interval, and the entry distance in the feed direction to achieve the best surface quality in a given machining time. Cutting tests are designed to verify the integrated optimization methodology. The experimental results show that, in the fabrication of plane surface, the changing of entry distance improves surface finish about 40 nm (R _a ) and 200 nm (R _t ) in vertical cutting and decreases about 8 nm (R _a ) and 35 nm (R _t ) in horizontal cutting with less than 2 s spending extra machining time. The optimal shift ratio decreases surface roughness about 7 nm (R _a ) and 26 nm (R _t ) in the fabrication of cylinder surfaces, while the total machining time only increases 2.5 s. This infers that the integrated optimization methodology contributes to improve surface quality without decreasing the machining efficiency in ultraprecision milling process.     

The study of machined surface roughness by random analysis

The effects of various parameters on surface roughness were studied by measuring R_a (c.l.a. value) or R_t (peak-to-valley height). The effect of cutting speed, feed, rake angle and depth of cut on the randomness of the surface profile were studied from the auto-correlation function of the surface profile.     

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.     

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.     

Effect of waterjet peening on aluminum alloy 5005

The present study addresses the effect of waterjet peening parameters on aluminum alloy 5005. The approach was based on the response surface methodology utilizing the Box–Behnken experimental design. Workable empirical models were developed to predict surface roughness (R _a ) and hardness (HV). Increasing the number of passes, pressure, and standoff distance produces a higher surface roughness as well as a higher hardness. On the contrary, increasing the feedrate produces a lower surface roughness and hardness. The developed empirical models for R _a and HV have reasonable correlations between the measured and predicted responses with acceptable coefficients of determinations. A different set of optimum parameters was generated based on different desirability functions for each response. The predicted and the actual responses for optimized R _a and HV are satisfactory with good reliability. It is shown that the models are workable in predicting the responses of R _a and HV in the present research. A proper selection of peening parameters can be formulated to be used in practical works.     

Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach

In the past, roughness values measured directly on machined surfaces were used to develop mathematical models that are used in predicting surface roughness in turning. This approach is slow and tedious because of the large number of workpieces required to obtain the roughness data. In this study, 2-D images of cutting tools were used to generate simulated workpieces from which surface roughness and dimensional deviation data were determined. Compared to existing vision-based methods that use features extracted from a real workpiece to represent roughness parameters, in the proposed method, only simulated profiles of the workpiece are needed to obtain the roughness data. The average surface roughness R _a, as well as dimensional deviation data extracted from the simulated profiles for various feed rates, depths of cut, and cutting speeds were used as the output of response surface methodology (RSM) models. The predictions of the models were verified experimentally using data obtained from measurements made on the real workpieces using conventional methods, i.e., surface roughness tester and a micrometer, and good correlation between the two methods was observed.     

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.     

Influence of surface preparation on roughness parameters,friction and wear

The aim of the present research was to investigate influence of surface preparation on roughness parameters and correlation between roughness parameters and friction and wear. First the correlation between different surface preparation techniques and roughness parameters was investigated. For this purpose 100Cr6 steel plate samples were prepared in terms of different average surface roughness, using different grades of grinding, polishing, turning and milling. Different surface preparation techniques resulted in different R_a values from 0.02 to 7 μm. After this, correlation between surface roughness parameters and friction and wear was investigated. For this reason dry and lubricated pin-on-disc tests, using different contact conditions, were carried out, where Al₂O₃ ball was used as counter-body. It was observed that parameters R_ku, R_sk, R_pk and R_vk tend to have influence on coefficient of friction.     

Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform

This paper presents a system for automated, non-contact, and flexible prediction of surface roughness of end-milled parts through a machine vision system which is integrated with an artificial neural network (ANN). The images of milled surface grabbed by the machine vision system could be extracted using the algorithm developed in this work, in the spatial frequency domain using a two-dimensional Fourier transform to get the features of image texture (major peak frequency F ₁, principal component magnitude squared value F ₂, and the average gray level G _a). Since F1 is the distance between the major peak and the origin, it is a robust measure to overcome the effect of lighting of the environment. The periodically occurring features such as feed marks and tool marks present in the gray-level image can be easily observed from the principal component magnitude squared value F _2. The experimental machining variables speed S, feedrate F, depth of cut D, and the response extracted image variables F ₁, F ₂, and G _a could be used as input data, and the response surface roughness R _a measured by Surfcorder SE-1100 (traditional stylus method) could be used as output data of an ANN ability to construct the relationships between input and output variables. The ANN was trained using the back-propagation algorithm developed in this work due to its superior strength in pattern recognition and reasonable speed. Using the trained ANN, the experimental result had shown that the surface roughness of milled parts predicted by machine vision system over a wide range of machining conditions could be got with a reasonable accuracy compared with those measured by traditional stylus method. Compared with the stylus method, the constructed machine vision system is a useful method for prediction of the surface roughness faster, with a lower price, and lower environment noise in manufacturing process. Experimental results have shown that the proposed machine vision system can be implemented for automated prediction of surface roughness with accuracy of 97.53%. The results are encouraging that machine vision system can be extended to many real-time industrial prediction applications.     

Investigation of machining parameters for the multiple-response optimization of micro electrodischarge milling

This paper investigated the influence of three micro electrodischarge milling process parameters, which were feed rate, capacitance, and voltage. The response variables were average surface roughness (R _a ), maximum peak-to-valley roughness height (R _y ), tool wear ratio (TWR), and material removal rate (MRR). Statistical models of these output responses were developed using three-level full factorial design of experiment. The developed models were used for multiple-response optimization by desirability function approach to obtain minimum R _a , R _y , TWR, and maximum MRR. Maximum desirability was found to be 88%. The optimized values of R _a , R _y , TWR, and MRR were 0.04, 0.34 μm, 0.044, and 0.08 mg min^?1, respectively for 4.79 μm s^?1 feed rate, 0.1 nF capacitance, and 80 V voltage. Optimized machining parameters were used in verification experiments, where the responses were found very close to the predicted values.     

Surface roughness and roundness of bearing raceway machined by floating abrasive polishing and their effects on bearing’s running noise

As the two most important indexes of bearing raceway, surface roughness and roundness have significant influence on bearing noise. Some researchers have carried out studies in this field, however, reason and extent of the influence of raceway surface geometric characteristics on bearing running noise are not perfectly clear up to now. In this paper, the raceway of 6309 type bearing＇s inner and outer ring is machined by floating abrasive polishing adopting soft abrasive pad. Surface roughness parameters, arithmetical mean deviation of the profile Ra, the point height of irregularities Rz, maximum height of the profile Rmax and roundness fof raceways, are measured before and after machining, and the change rules of the measured results are studied. The study results show that the floating abrasive polishing can reduce the surface geometric errors of bearing raceway evidently. The roundness error is reduced by 25%, Rm~x value is reduced by 35.5%, Rz value is reduced by 22% and Ra value is reduced by 5%. By analyzing the change of the geometrical parameters and the shape difference of the raceway before and after machining, it is found that the floating abrasive polishing method can affect the roundness error mainly by modifying the local deviation of the raceway＇s surface profile. Bearings with different raceway surface geometrical parameter value are assembled and the running noise is tested. The test results show that Ra has a little, Rmax and Rz have a measurable, and the roundness error has a significant influence on the running noise. From the viewpoint of controlling bearings＇ running noise, raceway roundness error should be strictly controlled, and for the surface roughness parameters, R,n~x and Rz should be mainly controlled. This paper proposes an effective method to obtain the low noise bearing by machining the raceway with floating abrasive polishing after super finishing.     

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.     

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.     

Optimisation of surface roughness on turning fibre-reinforced plastics (FRPs) with diamond cutting tools

Fibre-reinforced plastics (FRPs) are used in structural components in various fields of application of mechanical engineering, such as automobile, biomechanics and aerospace industries. Their own properties, particularly the high strength and stiffness and simultaneously low weight, allows the substitution of the metallic materials in many cases. As a result of these properties and potential applications, exist a great necessity to investigate the machining of these composite materials.This paper presents an optimisation study of surface roughness in turning FRPs tubes manufacturing by filament winding and hand lay-up, using polycrystalline diamond cutting tools. A plan of experiments was performed with cutting parameters prefixed in the FRP tubes. The objective was establishing the optimal cutting parameters to obtain a certain surface roughness (R_a and R_t/R_max), corresponding to international dimensional precision (ISO) IT7 and IT8 in the FRP workpieces, using multiple analysis regression (MRA). Additionally, the optimal material removal rates have been obtained.     

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.