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.     

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.     

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.     

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.     

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.     

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.     

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.     

Parametric optimization of CNC turning process for hybrid metal matrix composite

Machining of hybrid metal matrix composite is difficult as the particulates are abrasive in nature and they behave like a cutting edge during machining resulting in quick tool wear and induces vibration. An attempt was made in this experimental study to evaluate the machining characteristics of hybrid metal matrix composite, and a mathematical model was developed to predict the responses, namely surface finish, intensity of vibration and work-tool interface temperature for known cutting condition while machining was performed in computer numerical control lathe. Design of experiments approach was used to conduct the trials; response surface methodology was employed to formulate a mathematical model. The experimental study inferred that the vibration in V _x, V _y, and V _z were 41.59, 45.17, and 26.45 m/s², respectively, and surface finish R _a, R _q, and R _z were 1.76, 3.01, and 11.94 μm, respectively, with work-tool interface temperature ‘T’ of 51.74 °C for optimal machining parameters, say, cutting speed at 175 m/min, depth of cut at 0.25 mm and feed rate at 0.1 mm/rev during machining. Experimental results were in close conformity with response surface method overlay plot for responses.     

Selection of optimum cutting condition of cobalt-based superalloy with GONNS

Machining of new superalloys is challenging. Automated software environments for determining the optimal cutting conditions after reviewing a set of experimental results are very beneficial to obtain the desired surface quality and to use the machine tools effectively. The genetically optimized neural network system (GONNS) is proposed for the selection of optimal cutting conditions from the experimental data with minimal operator involvement. Genetic algorithm (GA) obtains the optimal operational condition by using the neural networks. A feed-forward backpropagation-type neural network was trained to represent the relationship between surface roughness, cutting force, and machining parameters of face-milling operation. Training data were collected at the symmetric and asymmetric milling operations by using different cutting speeds (V _c), feed rates (f), and depth of cuts (a _p) without using coolant. The surface roughness (Ra_asymt, Ra_symt) and cutting force (Fx_asymt, Fy_asymt, Fz_asymt, Fx_symt, Fy_symt, Fz_symt) were measured for each cutting condition. The surface roughness estimation accuracy of the neural network was better for the asymmetric milling operation with 0.4% and 5% for training and testing data, respectively. For the symmetric milling operations, slightly higher estimation errors were observed around 0.5% and 7% for the training and testing. One parameter was optimized by using the GONNS while all the other parameters, including the cutting forces and the surface roughness, were kept in the desired range.     

Determination of a coupling equation for milling parameters based on optimal cutting temperature

Nanofluid minimum quantity lubrication (NMQL) is one of the main modes of sustainable manufacturing. It is an environment-friendly, energy-saving, and highly efficient lubrication method. With the use of nanoparticles, the tribological properties of debris–tool and workpiece–tool interfaces will change. However, spectrum analyses of force and power spectral density (PSD) of surface microstructures are limited. In the present work, the milling force, friction coefficient, specific energy, surface roughness, and surface microstructure of debris were evaluated in milling of 45 steel for different lubrication conditions, namely, dry, flood, minimum quantity lubrication, and Al₂O₃ NMQL. Results demonstrated that compared with other lubrication conditions, NMQL achieves minimum milling force peak (F_x?=?270 N, F_y?=?160 N, F_z?=?50 N), friction coefficient (μ?= 1.039), specific energy (U?= 65.5 J/mm³), and surface roughness value (R_a?=?2.254 μm, RS_m?=?0.0562 mm). Furthermore, a spectrum analysis of the milling force and PSD of the surface microstructure was conducted for validation. The spectral analysis of milling force revealed that NMQL obtained the lowest milling force and amplitude in the middle-frequency region, thereby indicating the minimum abrasion loss of the tool. Meanwhile, the PSD analysis indicated that NMQL had the lowest proportional coefficient in the low-frequency region (0.4766) and the highest proportional coefficient in the high-frequency region (0.0569). These results revealed that the workpiece surface gained by Al₂O₃ NMQL obtained higher wave fineness than other working conditions. By combining with the lowest R_a, NMQL contributes the best workpiece surface quality. Therefore, machining experiments using NMQL showed the best lubrication performance.     

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.     

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.     

Comparative study of conventional and micro WEDM based on machining of meso/micro Sized Spur Gear

This paper discusses the comparison of micro machining process using conventional and micro wire electrical discharge machining (WEDM) for fabrication of miniaturized components. Seventeen toothed miniaturized spur gear of 3.5 and 1.2 mm outside diameter were fabricated by conventional and micro WEDM respectively. The process parameters for both conventional and micro WEDM were optimized by preliminary experiments and analysis. The gears were investigated for the quality of surface finish and dimensional accuracy which were used as the criteria for the process evaluation. An average surface roughness (R_a) of 50 nm and dimensional accuracy of 0.1–1 μm were achieved in micro WEDM. Whenever applied conventional WEDM for meso/micro fabrication, a R_a surface roughness of 1.8 μm and dimensional accuracy of 2–3 μm were achieved. However, this level of surface roughness and dimensional accuracy are acceptable in many applications of micro engineering. A window of conventional WEDM consisting of low energy discharge parameters is identified for micromachining.     

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.     

Experimental study on micro electrical discharge machining of porous stainless steel

Various cutter strategies have been developed during milling freeform surface. Proper selection of the cutter path orientation is extremely important in ensuring high productivity rate, meeting the better quality level, and longer tool life. In this work, finish milling of TC17 alloy has been done using carbide ball nose end mill on an incline workpiece angle of 30°. The influence of cutter path orientation was examined, and the cutting forces, tool life, tool wear, and surface integrity were evaluated. The results indicate that horizontal downward orientation produced the highest cutting forces. Vertical downward orientation provided the best tool life with cut lengths 90–380 % longer than for all other orientations. Flank wear and adhesion wear were the primary wear form and wear mechanisms, respectively. The best surface finish was achieved using an upward orientation, in particular, the vertical upward orientation. Compressive residual stresses were detected on all the machined surfaces, and vertical upward orientation provided the minimum surface compressive residual stress. In the aspect of tool wear reduction and improvement of surface integrity, horizontal upward cutter path orientation was a suitable choice, which provided a tool life of 270 m, surface roughness (R _a ) of 1.46 μm, and surface compressive residual stress of ?300 MPa.     

The effects of the silicon wafer resistivity on the performance of microelectrical discharge machining

In this study, the performance of Si wafer machining by employing the die-sinking microelectrical discharge machining technique is reported. Specifically, the machining performance was examined on both high- (1–10 Ω cm) and low-resistivity (0.001–0.005 Ω cm) Si wafers by means of using a range of discharge energies. In this regard, the machining time, material removal rate, surface quality, surface roughness, and material mapping, which are categorized among the important properties in micromachining, have been investigated. In order to analyze the surface properties and to perform the elemental analysis, the scanning electron microscope and energy-dispersive X-ray spectroscopy were used. In contrast, the 3D surface profiler was used to evaluate the roughness of machined surface. The results of this experimental study revealed that the electrical resistivity and discharge energy parameter of microelectrical discharge machining had a great influence on the Si wafer machining performances. The observations in this study indicated a decrease in machining time, high material removal rate, and high surface roughness with an increased discharge energy values. Overall, it was learnt that the minimum amount of energy required to machine Si wafer was 5 μJ for both low and high-resistivity Si. In addition, the highest material removal of 5.842 × 10^?5 mm³/s was observed for low-resistivity Si. On the contrary, the best surface roughness, R _a, of 0.6203 μm was achieved for high-resistivity Si and it also pointed to a higher carbon percentage after the machining process.     

The turning of overlays using sintered carbide tools

Surfacing technology is used to produce layers with special properties for new parts or for worn out functional surfaces. For many parts, it is necessary to machine them to fulfill the requirements for shape, sizes, and roughness, but the machining of overlays is, in comparison with the machining of drawn or rolled semi-products, specific. Usually, the optimization of the cutting conditions issues from the long-term tool-life test results. For these tests we have experimentally determined time dependences of tool wear at different cutting speeds for every combination of overlay and cutting material. These dependences are the basis for the determination of the relationship between tool life and cutting speed for the limit of the tool wear. The relationships between cutting speed and tool life determined in this way are the initial values for the own optimization of the cutting conditions. It is usual to determine the optimum for the minimum machining costs criterion. In the calculation, all economic indexes of the workshop are included. Measuring of the overlay hardness, HRC, and of the overlay surface roughness, R_a and R_t, after machining was also part of the tests. In this paper we publish the results of long-term tool-life tests made during the turning of an overlay made by welding on the wire C 508 using the tool from the firms Kennametal Hertel, Mircona, Sandvik Coromant, Walter and Widia. In the tests we used five types of tool material in form of inserts WNMA and WNMG type. The best results were achieved using the Kennametal Hertel WNMG 080412 KC 9315 insert. During the tests we found that the roughness of the turned surface slowly increases and that extensive deterioration occurs at the end of the tool life.     

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.     

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.