Material Removal Rate Model Research Articles

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy.

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (Ton), and pulse-off-time (Toff) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and Ton were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm3/s at Ton of 95 µs, Toff of 9 µs, current of 6 A. Least SR was obtained at Ton of 35 µs, Toff of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance.

Prediction of Surface Profile Evolution of Workpiece and Lapping Plate in Lapping Process

Abstract Lapping has a history of hundreds of years, yet it still relies on the experience of workers. To improve the automaticity and controllability of the lapping process, a modeling method of friction and wear is developed to predict the surface profile evolution of the workpiece and lapping plate in the lapping process. In the proposed method, by solving the balance equations of resultant force and moment, the inclination angles of the workpiece can be calculated; thus, a more accurate contact pressure distribution of the workpiece/lapping plate interface can be calculated. Combined with the material removal rate model, the continuous evolution process of the workpiece and lapping plate can be predicted in the lapping process. The modeling method was validated by a lapping test of a flat optical glass workpiece (Φ 100 mm) with a composite copper plate. The results show that the proposed method can predict the evolution of the surface profile of the workpiece and lapping plate with high accuracy. Consequently, the lapping plate can be dressed at the right time point. Benefit from this, in the validation test the Peak to Valley (PV) value of the workpiece (with 5 mm edge exclusion) was reduced from 5.279 µm to 0.267 µm in 30 min. The proposed surface profile evolution modeling method not only improves the lapping efficiency but also provides an opportunity to understand the lapping process.

Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes

This study focuses on obtaining regression models for material removal rate and tool wear in rough honing processes. For this purpose, experimental tests were carried out according to a central composite design of experiments. Five different parameters were varied: grain size or particle size of abrasive, density of abrasive or abrasive concentration, pressure of the stones against the cylinder internal surface, tangential speed (in this case, corresponding to the rotation speed of the cylinder), and linear speed of the honing head. In addition, multi-objective optimization was carried out with the aim of maximizing the material removal rate and minimizing tool wear. The results show that, within the range studied, the material removal rate depends mainly on tangential speed, followed by grain size and pressure. Tool wear is directly influenced by density of abrasive, followed by pressure, tangential speed, and grain size. According to the multi-objective optimization, if the two responses are given the same importance, it is recommended that high grain size, high density, high tangential speed, and low pressure be selected. Linear speed has less influence on both responses studied. If the material removal rate is considered to be more preponderant than tool wear, then the same values should be considered, except for high pressure. If tool wear is preponderant, then lower grain size of 128 (ISO 6106) should be selected, and lower tangential speed of approximately 166 min−1. The other variables, density and pressure, would not change significantly from the first situation.

Effects of quantities and pole-arrangements of magnets on the magneto-rheological polishing (MRP) performance of sapphire hemisphere

To improve the polishing effectiveness of sapphire hemisphere, the effects of quantities and pole-arrangements of magnets in magnetic field generator on the magneto-rheological polishing (MRP) performance of sapphire hemisphere were investigated through simulation and experiment. After analyzing the distribution of magnetic field on sapphire hemisphere surface and its change with the relative motion between sapphire surface and magnetic field generator during MRP, we developed a material removal rate (MRR) model based on the Preston equation, and the material removal distribution and average removal depth of polished sapphire surface could be predicted. Then according to the calculated material removal distribution, a surface roughness model of polished sapphire was established. Both simulation and experiment results showed that the MRR of polished sapphire increases with the quantity of magnets, while the surface roughness Ra of polished sapphire decreases with the quantity of magnets. As for the effect of pole-arrangement, the cross-stacking arrangement produced a surface of Ra 2.97 nm with MRR of 0.496 μm/h, and the cross-involute arrangement achieved a smoother surface of Ra 0.79 nm with MRR of 0.435 μm/h in MRP. These results showed that the cross-involute and cross-stacking pole-arrangements are favorable for the MRP of sapphire hemisphere. The good agreement between experimental and theoretical results verified the reliability of the established MRR and Ra models.

Green-chemical-jump-thickening polishing for silicon carbide

An ultra-precision machining approach called green-chemical-jump-thickening polishing (GC-JTP) is explored to improve surface accuracy and to achieve high-efficiency polishing for silicon carbide (SiC) ceramics. A new concept of green-chemical-jump-thickening polishing slurry (GC-JTPS) is developed to be an environmentally-friendly fluid with including two essential capabilities: one is the chemistry-induced jump-thickening (JT) mechanism and the other is green-chemical-thickening to enhance removal efficiency. Based on Navier-Stokes's equation and flow continuity equation, the polishing pressure in the GC-JTP process is calculated. On the basis of the rheological dynamics and abrasive nano-indentation analysis, a predictive understanding model to assess material removal rate (MRR) is proposed for the machining controllability of GC-JTP. Under the GC-JTP verification experiments, the maximal proportional error between the testing results and the theoretical calculation stands at only ζ = 7.8%, which proves the accuracy and effectiveness of the new theory MRR in GC-JTP. Through the fitting error, the correction coefficient of α = 0.929 should be used in the MRR model in order to better guide the actual processing. The analysis of the maximal cutting depth and the threshold of ductile-brittle transition revealing subsurface damages might generate. Under the optimized polishing conditions of the shearing velocity v r = 2.0 m/s, the abrasive size D a = 2.0 μm, the gap depth δ 0 = 1.5 mm, the sodium bicarbonate W p = 15 wt%, the sorbitol W o = 20 wt% and the abrasive CeO 2 W a = 30 wt%, the SiC ceramics roughness is reduced from Ra 768 nm to Ra 32 nm and the subsurface damages depth can be controlled at the range of 2.5 μm–4.5 μm to achieve good surface integrity. The work demonstrates that the GC-JTP is a green and efficient processing method for the low-damage polishing of SiC ceramics.

Evaluation of different approach for WEDM process optimization

In this paper, an effective process optimization approach based on artificial neural networks with a backpropagation and response surface methodology including central composite design is presented for the modeling and prediction of material removal rate in the wire electrical discharge machining process. In the development of predictive models, cutting parameters of pulse on time, pulse off time, peak current, spark gap set voltage, wire tension, and wire feed rate are considered as model variables. After experiments are carried out, the BPNN model was developed using six process parameters. The performance of the developed artificial neural networks and response surface methodology predictive models is tested for prediction accuracy in terms of the coefficient of determination and root mean square error metrics. MRR's CCD statistical models were compared to the performance of the created BPNN models. On 52 investigative data points, the training of a neural network model based on back-propagating was evaluated. The results indicate that an artificial neural networks model provides a more accurate prediction than the response surface methodology model.

Optimization of material removal rate of Ti-6Al-4V using Rao-1 algorithm

Titanium alloy Ti6Al4V has attractive properties for engineering applications. Electric discharge machining is nowadays widely adapted for titanium alloy machining to produce proper geometric profiles and surface integrity. EDM has a high machining cost and has a lower material removal rate (MRR). It is always desirable to improve the machining efficiency. This research involves, enhancing the MRR of Titanium alloy during EDM. The influence of four EDM input parameters on MRR is investigated in this research. Current, pulse on time, voltage and duty factor is selected as EDM control parameters. Response surface methodology (RSM) is utilized to create a design of experiment (DOE) plan. Thirty experiments were conducted as per the DOE. The experimental results are used to develop a predictable quadratic regression model for MRR using RSM. This MRR regression model is optimized using an advanced optimization technique, the Rao-1 algorithm. This is a new algorithm and easier to implement. The study reveals current and voltage are identified as the most influential factors in the MRR.

Comparative study on EDM process parameters optimization using BBO and ACO algorithms

The experimental study has been conducted on AISI 304 stainless steel in Electrical Discharge Machining (EDM) using Tungsten carbide electrode according to L27 orthogonal array. The main EDM input parameters name as pulse on time (P), discharge current (I), gap voltage (V) are chosen for exploring the effects on material removal rate (MRR) and surface roughness (SR). The main aim of this paper is to find out EDM optimum input parameters where the material removal rate (MRR) is maximized and while surface roughness (SR) is minimized. The regression model of MRR and SR was developed in statistical software using the experimental data. The developed regression models are optimized using two different optimization techniques namely Biogeography-Based Optimization (BBO) and Ant Colony Optimization (ACO).From these two optimization techniques, it has been observed that the BBO algorithm is more accurate than ACO. Finally a conformation test is performed to validate the BBO algorithm to obtain results and observed results showed satisfactory accuracy.

Ensemble Machine learning based performance prediction of electrical discharge machining for AISI D6 tool steel

Die and mould makers frequently employ AISI-D6 steel. First, 32 trials of electrical discharge machining (EDM) are carried out on the aforementioned material. A variety of EDM procedure performance measures, including tool wear ratio (TWR), material removal rate (MRR), and surface roughness (Ra), are taken into account, as well as the pulse time (Ton), pulse current (I), and pulse voltage (V). Operators are guided by process. Increasing MRR and Ra concentrations and decreasing TWR concentrations were found to increase pulse on-time values. As a result of increasing the pulse current, physical removal rate, tool wear ratio and surface unevenness increased. As a result of the increased voltage, the MRR, TWR, and Ra values were all reduced. The outcomes are predicted using ensemble machine learning models. The experimental data used to create estimation models for MRR, TWR, and Ra. In addition, an unknown set of experiments has validated the model-based predictions.

Multi-objective optimization of Abrasive water jet Machining parameters for Inconel 625 alloy using TGRA

It is discussed in this article how the Taguchi grey relational analysis (TGRA) was used on Inconel-625, a high-performance engineering material with a broad variety of industrial applications, by machining it through abrasive water jet machining. Abrasive water jet machining (AWJM) has been shown to be capable of imitating the machining capabilities of Inconel 625, as shown in this study by the use of the TGRA approach for multi objective optimization of its machining parameters. Taguchi method is used to model the influence of three input parameters, namely Standoff distance (SOD), Abrasive mass flow rate (AMF), and Transverse Speed (TS), on three selected responses - material removal rate (MRR) and surface roughness (SR). The regression equations, mathematical models, and contribution of the selected parameters are obtained, and it is found that they have an impact on the three chosen output responses. A link between the MRR, SF and Kf was established through the application of multi-objective optimization. It has been shown that SOD, TS, and AMF all have statistically significant impacts on MRR; however, TS has a substantial, but less significant, influence on SF and Kerf when compared to SOD and AMF. It was revealed via the use of Grey rational grades that when MRR grew, it resulted in inferior SF and greater Kf values. As a result, using regression analysis, a mathematical model for MRR, SF, and Kf was developed in order to achieve the optimal conditions. The parameters: Standoff distance, 1 mm; Mass flow rate, 400 gm/min; and Transverse speed have maximum grey relational grade and hence are the optimal parameter values generating higher machining performance in terms of cutting material removal rate and surface roughness. As a result, the potential for optimising the production process by the AWJM for Inconel-625 using TGRA has been effectively proven.

Machinability of tool steels machined by electric-discharge machining

SKD11 has been a widely used tool steel for cold works for many years while DC53 tool steel is intently developed to replace SKD11. In this paper, the machinability of these two tool steels was compared in terms of material removal rate (MRR), electrode wear ratio (EWR), and arithmetic mean of value of roughness (Ra) of the specimen machined by electric-discharge machining. The machining variables involved were electric-discharge time and electric-discharge peak current. The machining electrode was made from a copper alloy. Results show that both electric-discharge time and electric-discharge peak current had an influential effect on MRR, EWR, and Ra of both tool steels. MRR of SKD11 was significantly higher than that of DC53 while DC53 caused higher EWR than SKD11. It was found that SKD11 provided a better surface finish than DC53. Empirical models for MRR, EWR, and Ra were also presented in this paper.

Influence of different tool electrodes on machining of oil hardened non shrinking steel using die sink electric discharge machine

Tool steel has been a crucial material for many industrial applications and manufacturing of machining tools and milling cutters although very few machining techniques are available for machining of these materials. In this study, the performance of copper and brass tool electrodes on the die sink electric discharge machining of oil-hardened non shrinking steel (OHNS) has been evaluated. Current, pulse on time, and pulse off time were considered as the factors and the responses as material removal rate (MRR) and tool wear rate (TWR). The design of experiments technique namely the Box-Behnken design (BBD) under response surface methodology (RSM) was utilized. The response models were developed for MRR and TWR for brass and copper tool electrodes. The MRR and TWR for brass and copper tool electrodes were compared and represented graphically. The results show that current is the significant factor affecting both MRR and TWR and compared to brass, copper is a suitable electrode for die sink electric discharge machining of OHNS. The confirmative tests validate the developed response models of MRR and TWR.

Experimental analysis of surface finishing properties in magnetically assisted abrasive finishing of ASTM B16 brass

Magnetically assisted abrasive finishing (MAAF) presents an attractive concept of surface and edge finishing by fine magnetic abrasive particles (MAPs). This study aims to contribute an experimental evaluation of the effect of process parameters viz. magnetic field density (MFD), circumferential speed of workpiece, and abrasive grit size on the surface finishing properties in MAAF when experiments were performed for finishing pipes of ASTM B16 brass material with the sintered MAPs. The developed model is based on the obtained experimental data accompanied by Box- Behnken design (BBD) of response surface methodology (RSM) analysis. Apart from deciding significant parameters, this analysis also presents the modeling of finishing properties and optimizes the desired performance parameters. Analysis of variance (ANOVA) includes data of standard deviation, coefficient of determination (R2), adjusted, and predicted (R2). MFD and speed show a significant effect on both the responses viz. surface roughness improvement rate (SRIR) and material removal rate (MRR). Analysis has shown that abrasive grit size is the most dominant parameter towards SRIR followed by MFD. The maximum SRIR of 88.12% (minimum Ra 50 nm) and 4.28 mg min−1 is achieved through multi-objective optimization with 0.8 T MFD, 500 rpm speed, and 300 μm grit size. The mathematical models of SRIR and MRR were also developed using RSM, focusing on varying MFD, speed, and grit size which can be used to predict the desired surface finishing properties. The model generated for SRIR, and MRR has an error of 0.204% and 2.506% respectively. Further SEM images were taken to understand the surface appearance of the finished surface.

A novel Non-Newtonian fluid polishing technique for zirconia ceramics based on the weak magnetorheological strengthening thickening effect

Zirconia ceramics have excellent physical and chemical properties and have several applications. However, owing to their hardness and brittleness, it is difficult to realise the non-destructive high-efficiency machining of zirconia ceramics. Herein, we propose an efficient non-Newtonian fluid polishing method for zirconia ceramics based on the weak magnetorheological strengthening thickening effect. The nanoscale material removal and evolution mechanisms of the zirconia ceramic surface were investigated. Based on the results obtained through scanning electron microscopy and a derivation of the fluid shear stress model in the polishing zone, we clarify the microscopic material removal mechanism during the machining process, and establish the prediction model of material removal rate. The detection results of the surface quality were in good agreement with the established material removal mechanism. The model of material removal rate could predict the processing effectively, and the average relative error was 4.45%. The experimental results demonstrate that the proposed weak magnetorheological shear thickening polishing (WMRSTP) method can realise lossless, self-adaptive, ultra-precision machining of a hemispherical zirconia ceramic workpiece. Compared to the traditional non-Newtonian fluid polishing method, the polishing quality and polishing efficiency of this method are significantly improved. The surface roughness Sa, the surface accuracy PV, and the material removal rate MRR of the zirconia hemispherical workpiece after 1 h of WMRSTP were 13.2 nm, 0.104 μm, and 5.96 μm/h, respectively.

Modeling of forces and material removal rate in ultrasound assisted magnetorheological polishing (UAMP) of sapphire

In order to fully investigate the polishing performance of the Fe 3 O 4 /SiO 2 core-shell abrasive for sapphire material, it is necessary to conduct a comprehensive study into polishing forces and establish corresponding mathematical models. In this paper, based on the bonding strength of core-shell abrasive chains and the motion analysis of ultrasonic vibration in UAMP process, the mathematical models of shear force F τ and normal force F N generated on sapphire surface are established. Experiments were implemented to verify the reliability of the F τ and F N models under varying parameters, including the volume concentration of core-shell abrasives, excitation gap and working gap. The values and the variation trends of theoretical results are well in agreement with those of experimental results. Then, on the basis of the F τ and F N models and Preston equation, we proposed a modified material removal rate (MRR) model, in which the contributions of F N and F τ are all taken into consideration. Through the comparative analysis with the experimental results, the modified MRR model exhibits higher prediction accuracy than those models involving only F τ or F N . The high-precision MRR model will facilitate the deterministic removal of sapphire surface material in UAMP process. • Mathematical models of shear force and normal force in sapphire UAMP are established. • The reliability of the force models is verified by targeted experiments. • A modified MRR model based on the synergy of shear and normal force is developed. • The modified MRR model exhibits higher prediction accuracy than general MRR models.

Assessment on the Optimum Performance of a Novel 3 Axes Micro EDM Developed for Commercial Applications

Abstract A low cost three axes commercial microEDM machine has been developed indigenously in order to implement precision micromachining. The developed microEDM consists of various components such as machining chamber, X-Y table, tool feed and tool holder assembly, dielectric circulation system, power supply and automatic driving devices to control the spark gap distance. The setup is capable of performing the 8µm movement in Z-axis.The developed 3 axes machine and the commercial available machine are investigated with the most important process parameters such as Current, Pulse on time and Pulse off time in order to study the Material Removal Rate (MRR) and Tool Wear Rate (TWR). The material chosen for the current research is Stainless Steel 316L, which has high corrosion resistance and the electrode was tungsten 300µm. In most of the experiments, the developed machine has performed with 6.52% higher MRR and 26.10% Lower TWR. This is due to the effective spark gap circuit developed with the use of hall current sensor.The mathematical model of MRR and TWR are obtained by correlating the process parameter using Response Surface Methodology (RSM).The chosen objectives are contradictory in nature, we employ a non-dominated Genetic algorithm to find the optimal process parameters. The developed 3 axes microEDM has yield a 10.14% and 33.12% better performance on optimum maximum MRR and minimum TWR respectively when compared to the commercial machine.

Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite.

The mechanical, physical and interfacial properties of aluminum alloys are improved by reinforcing the silicon carbide particles (SiCp). Machinability of such alloys by traditional methods is challenging due to higher tool wear and surface roughness. The objective of research is to investigate the machinability of SiCp reinforced Al6061 composite by Wire-Electrical Discharge Machining (wire-EDM). The effect of wire-EDM parameters namely current (I), pulse-on time (Ton), wire-speed (Ws), voltage (Iv) and pulse-off time (Toff) on material removal rate (MRR) is investigated and their settings are optimized for achieving the high MRR. The experiments are designed by using Taguchi L16 orthogonal arrays. The MRR obtained at different experiments are analyzed using statistical tools. It is observed that all the chosen process parameters showed significant influence of on the MRR with contribution of 27.39%, 22.08%, 21.32%, 15.76% and 12.94% by I, Iv, Toff, Ton and Ws, respectively. At optimum settings, the Wire-EDM resulted in MRR of 65.21 mg/min and 62.41 mg/min for samples with 4% and 8% SiCp. The results also indicated reinforcing SiCp upto 8% showed marginally low influence on MRR. Microstructural investigation of the cut surface revealed the presence of craters with wave pattern on its surface. The top surface of the crater is featured by the recast layers connecting adjacent craters. Further, the statistical model is developed using linear regression to predict the MRR (𝑅2—73.65%) and its predicting accuracy is verified by the confirmation trials. The statistical model is useful for predicting the MRR for different settings of the process parameters. The optimized settings can be used to improve the machining productivity by increasing the MRR while machining of Al6061-SiCp (upto 8 wt. %) alloy by wire-EDM industries.

Modelling using dimensional analysis and optimization with grey-taguchi based fuzzy approach in s-eddg of skd-11 steel

The SKD-11 steel was machined by the hybrid surface-electro discharge diamond grinding (S-EDDG) machining process with an aim to determine the optimum setting of process parameters for multi-output optimization and thereafter developed the mathematical-models of material-removal-rate (MRR), and surface-roughness (Ra) through the Dimensional Analysis method. In this research work, Grey-Taguchi based Fuzzy method is utilized for multi-output optimization of process parameters. The various process-variables selected in this research work are current, voltage, wheel speed, pulse-on-time, duty-cycle, and nozzle flushing aid. Total 18 experiments have been conducted on S-EDDG set-up according to Taguchi’s L18 orthogonal array. The response table and Analysis of Variance investigation are used to determine the optimum setting of process control-parameters and further help to determine the impact of these control-factors on the multi-output performance index (MPI). In this research work, the Dimensional Analysis method is utilized for the development of mathematical models of MRR, and Ra. The experimental results and predicted estimation of values from the developed models were compared and showed satisfactory matching between them. The optimum combination of process variables suggested by the hybrid optimization method was validated through confirmation experiment whose result depicts that aimed MPI is significantly improved by 0.435. The suggested optimum setting of process variables helps the production engineer to set-up the economical-cum-efficient process.

Grinding aided electrochemical discharge drilling (G-ECDD): A theoretical analysis and mathematical modelling of material removal rate

Grinding aided electrochemical discharge drilling (G-ECDD): A theoretical analysis and mathematical modelling of material removal rate