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.     

Improvement of grinding performance and investigation of cutting parameters using a grinding mechanism with secondary rotational axis

“Grinding Mechanism having Advanced Secondary Rotational Axis” (GMASRA) is one of the newer plane surface grinding methods that have an uncommon abrasion mechanism. Unlike conventional methods, in GMASRA, there are two rotations of a wheel. The first rotation is the same as in conventional grinding methods, which is the circumferential rotation. The other rotation is the newly developed axial rotation, where the wheel rotates around itself perpendicular to its radial axis. In this study, the effects of certain cutting parameters on arithmetical mean deviation of the assessed profile (the R_a parameter) were investigated. Particularly, the effects of cutting parameters on R_a in the GMASRA grinding process were examined. The selected cutting parameters were the depth of cut, the number of axial revolutions of the wheel, and the stepover distance of the wheel. Five wheels with different properties were chosen. Additionally, GMASRA was modeled using the Taguchi orthogonal test design. In this orthogonal design, the depth of cut, the spindle speed, and the type of grinding wheel were chosen as the control factors. The effect of the specified control factors on the surface roughness was demonstrated using an analysis of variance (ANOVA) test. Results show that GMASRA produced better R_a values than the conventional method. R_a values were very close to each other in every part of the ground workpieces. According to the modeling results, the spindle speed had the highest effect on R_a, followed by the depth of cut and the type of grinding wheel. GMASRA is also very cost effective and can be adapted to most milling machines and CNC milling machines.     

Optimum selection of abrasive flow machining conditions during fine finishing of Al/15 wt% SiC-MMC using Taguchi method

Abrasive flow machining (AFM) is gaining widespread application finishing process on difficult to reach surfaces in aviation, automobile, and tooling industry. Al/SiC_p-MMC is a promising material in these industries. Here, AFM has been used to finish conventionally machined cylindrical surface of Al/15 wt% SiC_p-MMC workpiece. This paper presents the utilization of robust design-based Taguchi method for optimization of AFM parameters. The influences of AFM process parameters on surface finish and material removal have been analyzed. Taguchi experimental design concept, L₁₈ (6¹?×?3⁷) mixed orthogonal array is used to determine the S/N ratio and optimize the AFM process parameters. Analysis of variance and F test values also indicates the significant AFM parameters affecting the finishing performance. The mathematical models for R _a, R _t, ΔR _a, and ΔR _t and material removal are established to investigate the influence of AFM parameters. Conformation test results verify the effectiveness of these models and optimal parametric combination within the considered range. Scanning electron micrographs testifies the effectiveness of AFM process in fine finishing of Al/15 wt% SiC_p-MMC.     

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.     

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.     

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.     

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.     

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.     

The ability of precision hard turning to increase rolling contact fatigue life

In this paper, precision hard turning is proposed for the finishing of the AISI 52100 bearing components to improve rolling contact fatigue life. This finishing process induces a homogenous microstructure at surface and subsurface layers. Fatigue life tests performed on a twin-disk machine show that rolling contact fatigue life increases as R_a value decreases. The bearing components reached 0.32 million cycles for R_a=0.25 μm and 5.2 million cycles for R_a=0.11 μm. In comparison, the bearing components achieved 1.2 million cycles with grinding (R_a=0.2 μm) and 3.2 million cycles with grinding followed by honing (R_a=0.05 μm) respectively.     

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.     

Automated surface finishing of plastic injection mold steel with spherical grinding and ball burnishing processes

This study investigates the possibilities of automated spherical grinding and ball burnishing surface finishing processes in a freeform surface plastic injection mold steel PDS5 on a CNC machining center. The design and manufacture of a grinding tool holder has been accomplished in this study. The optimal surface grinding parameters were determined using Taguchi’s orthogonal array method for plastic injection molding steel PDS5 on a machining center. The optimal surface grinding parameters for the plastic injection mold steel PDS5 were the combination of an abrasive material of PA Al₂O₃, a grinding speed of 18000 rpm, a grinding depth of 20 μm, and a feed of 50 mm/min. The surface roughness R_a of the specimen can be improved from about 1.60 μm to 0.35 μm by using the optimal parameters for surface grinding. Surface roughness R_a can be further improved from about 0.343 μm to 0.06 μm by using the ball burnishing process with the optimal burnishing parameters. Applying the optimal surface grinding and burnishing parameters sequentially to a fine-milled freeform surface mold insert, the surface roughness R_a of freeform surface region on the tested part can be improved from about 2.15 μm to 0.07 μm.     

Study on the nano-powder-mixed sinking and milling micro-EDM of WC-Co

Present study investigates the feasibility of improving surface characteristics in the micro-electric discharge machining (EDM) of cemented tungsten carbide (WC?CCo), a widely used die and mould material, using graphite nano-powder-mixed dielectric. In this context, a comparative analysis has been carried out on the performance of powder-mixed sinking and milling micro-EDM with view of obtaining smooth and defect-free surfaces. The surface characteristics of the machined carbide were studied in terms of surface topography, crater characteristics, average surface roughness (R _a) and peak-to-valley roughness (R _max). The effect of graphite powder concentration on the spark gap, material removal rate (MRR) and electrode wear ratio (EWR) were also discussed for both die-sinking and milling micro-EDM of WC?CCo. It has been observed that the presence of semi-conductive graphite nano-powders in the dielectric can significantly improve the surface finish, enhance the MRR and reduce the EWR. Both the surface topography and crater distribution were improved due to the increased spark gap and uniform discharging in powder-mixed micro-EDM. The added nano-powder can lower the breakdown strength and facilitate the ignition process thus improving the MRR. However, for a fixed powder material and particle size, all the performance parameters were found to vary significantly with powder concentration. Among the two processes, powder-mixed milling micro-EDM was found to provide smoother and defect-free surface compared to sinking micro-EDM. The lowest value of R _a (38?nm) and R _max (0.17???m) was achieved in powder-mixed milling micro-EDM at optimum concentration of 0.2?g/L and electrical setting of 60?V and stray capacitance.     

An experimental study on microcutting of silicon using a micromilling machine

Micro-end-milling can potentially create desired 3D free-form surface features on silicon using ductile machining technology. A number of technological barriers must be overcome for micro-end-milling to be applied in the cutting of single crystal silicon. To produce smooth surfaces on brittle materials, such as silicon, it is important that the material be machined in the ductile mode. A major limitation of machining brittle materials is that the process of removing the material can generate subsurface damage. We have carried out an experimental study to find the optimum cutting conditions for obtaining ductile regime machining using a micromilling machine. The ductile and brittle regimes in the machining of silicon using diamond-coated end mills were demonstrated by machining grooves. The force ratio, F_t/F_c, was used to determine the milling performance on silicon. The experimental data show that the dominant ductile cutting mode was achieved when F_t/F_c?>?1.0.     

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.     

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.     

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.     

Surface finish of bulk metallic glass using sequential abrasive jet polishing and annealing processes

Surface finish plays an important role in product quality due to its direct effects on product appearance. Hence, improvement of the surface finish is an essential requirement in industrial products. In an attempt to improve the surface finish of bulk metallic glass (BMG) material, several common methods have been used, such as milling, grinding, and lapping. However, the BMG surface finish has not yet been significantly improved by using these methods. Therefore, this paper proposes sequential abrasive jet polishing (AJP) and annealing processes that can considerably improve the BMG surface finish. In addition, this paper also takes into account optimal parameters for the AJP and annealing processes based on the Taguchi’s L ₁₈ and L ₈ orthogonal array experimental results, respectively. The experimental results show that using optimal AJP parameters, the surface roughness (R _a) of the ground specimens can be significantly improved from 0.675 to 0.016 μm. After the AJP process, the surface roughness (R _a) of the polished specimens can be further improved from 5.7 to 2 nm within an area of 5?×?5 μm by using optimal annealing parameters.     

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.     

METHODOLOGY TO COMPARE 3-D AND 2-D PARAMETERS FOR THE OPTIMIZATION OF HARD TURNED SURFACES

ABSTRACT

Hard Turning provides an alternative to grinding in some finishing applications. Average Surface Roughness R_a has been widely used in industry to establish surface texture needed for a given application. It is known that the single parameter R_a is inadequate to define the functionality of a surface texture. The quality of a surface can be determined by the nature of its interaction with another surface. Thus a surface with significant peaks will not make as good a bearing surface as a surface with deep valleys and low peaks. Two different surfaces with similar values of R_a can behave differently under fatigue loading conditions. 3-D visualization of expected surface texture will facilitate optimization of machining parameters to produce function-specific surfaces. The advantages and shortcomings of some current surface texture prediction models are discussed. A new method based on neuro-fuzzy techniques is proposed. Optimization using some 3-D surface parameters was carried out and compared with the results of those obtained using 2-D parameters.