Thermal modeling of the material removal rate and surface roughness for die-sinking EDM

The die-sinking electrical discharge machining (EDM) process is characterized by slow processing speeds. Research effort has been focused on optimizing the process parameters so as for the productivity of the process to be increased. In this paper a simple, thermal based model has been developed for the determination of the material removal rate and the average surface roughness achieved as a function of the process parameters. The model predicts that the increase of the discharge current, the arc voltage or the spark duration results in higher material removal rates and coarser workpiece surfaces. On the other hand the decrease of the idling time increases the material removal rate with the additional advantage of achieving slightly better surface roughness values. The model’s predictions are compared with experimental results for verifying the approach and present good agreement with them.     

Modelling of surface finish,electrode wear and material removal rate in electrical discharge machining of hard-to-machine alloys

Hard-to-machine alloys are commonly used for industrial applications in the aeronautical, nuclear and automotive sectors, where the materials must have excellent resistance to corrosion and oxidation, high temperature resistance and high mechanical strength. In this present study the influence of different parameters of the electrical discharge machining process on surface roughness, electrode wear and material removal rate have been studied. Regression techniques are employed to model arithmetic mean deviation Ra (μm), peak count Pc (1/cm), material removal rate MRR (mm³/min) and electrode wear EW (%). All these parameters have been studied in terms of current intensity supplied by the generator of the electrical discharge machine I (A), pulse time t_i (μs), duty cycle η and open-circuit voltage U (V). This modelling allows us to obtain mathematical data and models to predict that the most influential factor in MRR and Ra is the current intensity and in the case of EW and Pc is the pulse time.     

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.     

Electrode wear and material removal rate during EDM of aluminum and mild steel using copper and brass electrodes

In the present study an analysis has been done to evaluate the electrode wear along the cross-section of an electrode compared to the same along its length during EDM of aluminum and mild steel using copper and brass electrodes. In an overall performance comparison of copper and brass electrodes, we found that electrode wear increases with an increase in both current and voltage, but wear along the cross-section of the electrode is more compared to the same along its length. This is due to easier heat transfer along the length compared to the same along the cross-section of the electrode. It was also found that the wear ratio increases with an increase in current. That means, though a higher current causes more removal of work material and the electrode, comparatively more material is removed from the electrode. The highest wear ratio was found during machining of steel using a brass electrode. The low thermal conductivity of brass electrodes causes less heat loss, and its low melting point results in fast melting of the electrode material. At the same time, low thermal conductivity of steel results in poor heat absorption, and its high melting temperature causes poor removal of work material. These factors result in the highest wear ratio during machining of steel using a brass electrode. The highest material removal rate was observed during machining of aluminum using brass electrodes. Comparatively low thermal conductivity of brass as an electrode material does not allow the absorption of much heat energy, and most of the heat is utilized in the removal of material from aluminum workpiece at a low melting point. But during machining of steel using copper electrodes, a comparatively smaller quantity of heat is absorbed by the work material due to its low thermal conductivity. As a result material removal rate becomes very low.     

Multi-objective optimization of material removal rate and surface roughness in wire electrical discharge turning

Wire electrical discharge turning (WEDT) is an emerging area, and it can be used to generate cylindrical forms on difficult to machine materials by adding a rotary axes to WEDM. The selection of optimum cutting parameters in WEDT is an important step to achieve high productivity while making sure that there is no wire breakage. In the present work, the WEDT process is modelled using an artificial neural network with feed-forward back-propagation algorithm and using adaptive neuro-fuzzy inference system. The experiments were designed based on Taguchi design of experiments to train the neural network and to test its performance. The process is optimized considering the two output process parameters, material removal rate, and surface roughness, which are important for increasing the productivity and quality of the products. Since the output parameters are conflicting in nature, a multi-objective optimization method based on non-dominated sorting genetic algorithm-II is used to optimize the process. A pareto-optimal front leading to the set of optimal solutions for material removal rate and surface roughness is obtained using the proposed algorithms. The results are verified with experiments, and it is found to improve the performance of WEDT process. Using this set of solutions, required input parameters can be selected to achieve higher material removal rate and good surface finish.     

A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert

The prime factor for selecting equipment is its performance capability and reliability without compromising on quality. Materials for aerospace application such as aluminum and its alloys have limited applications because of their complications in machining, effectively and economically. There is no further development in raising the effectiveness above the optimal level in cutting tool materials. The surface roughness influences the determination of the quality of the product. The present study focuses on finding optimal end milling process parameters by considering multiple performance characteristics using grey fuzzy approach. In this work, Aluminum Alloy 6082T6 (AA6082T6) is used as workpiece material which was end milled using Aluminum Chromo Nitride (AP3) coated milling insert. Three process performance parameters namely Centre Line Average Roughness (Ra), Root Mean Square Roughness (Rq) and Material Removal Rate (MRR) were optimized. The grey output is fuzzified into five membership functions and also with twenty-seven rules. Grey Fuzzy Reasoning Grade (GFRG) is developed and the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) shows that the maximum contribution in the depth cut is (31.785%) followed by feed (28.212%). Moreover, Adaptive Neuro-Fuzzy Inference System (ANFIS) model has been developed with the help of the same input values compared to the performance of the fuzzy logic model. With the help of detailed analysis, it has been found that the fuzzy logic based model gives more reasonable results when compared to ANFIS model.     

Experimental and finite element analysis of EDM process and investigation of material removal rate by response surface methodology

In this study, thermal modeling and finite element simulation of electrical discharge machining (EDM) has been done, taking into account several important aspects such as temperature-dependent material properties, shape and size of the heated zone (Gaussian heat distribution), energy distribution factor, plasma flushing efficiency, and phase change to predict thermal behavior and material removal mechanism in EDM process. Temperature distribution on the cathode has been calculated using ANSYS finite element code, and the effect of EDM parameters on heat distribution along the radius and depth of the workpiece has been obtained. Temperature profiles have been used to calculate theoretical material removal rate (MRR) from the cathode. Theoretically calculated MRRs are compared with the experimental results, making it possible to precisely determine the portion of energy that enters the cathode for AISI H13 tool steel. Also in this paper, the effect of EDM parameters on MRR has been investigated by using the technique of design of experiments and response surface methodology. Finally, a quadratic polynomial regression model has been proposed for MRR, and the accuracy of this model has been checked by means of analysis of residuals.     

Surface roughness and material removal rate of lapping process on ceramics

Lapping is a widely used surface finishing process for ceramics. An experimental investigation is conducted into the lapping of alumina, Ni−Zn ferrite and sodium silicate glass using SiC abrasive to study the effect of process parameters, such as abrasive particle size, lapping pressure, and abrasive concentration, on the surface roughness and material removal rate during lapping. A simple model is developed based on the indentation fracture and abrasive particle distribution in the slurry to explain various aspects of the lapping process. The model provides predictions for the surface roughness,R _a andR _t , on the machined surface and rough estimation for the material removal rate during lapping. Comparison of the predictions with the experimental measurements reveals same order of magnitude accuracy.     

Material removal rate and electrode wear ratio study on the powder mixed electrical discharge machining of cobalt-bonded tungsten carbide

In this article, a material removal rate (MRR) and electrode wear ratio (EWR) study on the powder mixed electrical discharge machining (PMEDM) of cobalt-bonded tungsten carbide (WC-Co) has been carried out. This type of cemented tungsten carbide was widely used as moulding material of metal forming, forging, squeeze casting, and high pressure die casting. In the PMEDM process, the aluminum powder particle suspended in the dielectric fluid disperses and makes the discharging energy dispersion uniform; it displays multiple discharging effects within a single input pulse. This study was made only for the finishing stages and has been carried out taking into account the four processing parameters: discharge current, pulse on time, grain size, and concentration of aluminum powder particle for the machinability evaluation of MRR and EWR. The response surface methodology (RSM) has been used to plan and analyze the experiments. The experimental plan adopts the face-centered central composite design (CCD). This study highlights the development of mathematical models for investigating the influence of processing parameters on performance characteristics.     

A model for wear and surface roughness transients during the running-in of bearings

Experimental results obtained during the initial operation of a bearing were used to obtain correlations of the linear wear rate and the surface roughness change with the surface roughness. Polynomial expressions of these correlations were used to obtain a differential law and finally analytical expressions of the linear wear as a function of the sliding distance and the initial surface roughness.The model offers a quantitative explanation of previous observations on the importance of the initial surface roughness on the surface roughness and wear history during running-in.     

Development of an intelligent process model for EDM

This paper reports the development of an intelligent model for the electric discharge machining (EDM) process using finite-element method (FEM) and artificial neural network (ANN). A two-dimensional axisymmetric thermal (FEM) model of single-spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time- and energy-dependent spark radius, etc. to predict the shape of crater cavity, material removal rate, and tool wear rate. The model is validated using the reported analytical and experimental results. A neural-network-based process model is proposed to establish relation between input process conditions (discharge power, spark on time, and duty factor) and the process responses (crater geometry, material removal rate, and tool wear rate) for various work—tool work materials. The ANN model was trained, tested, and tuned using the data generated from the numerical (FEM) simulations. The ANN model was found to accurately predict EDM process responses for chosen process conditions. It can be used for the selection of optimum process conditions for EDM process.     

Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide

Cemented carbide has been investigated as a useful material for the fabrication of micro devices. Focused ion beam (FIB) micro-milling has been found to be one of the most appropriate methods for the fabrication of micro devices. The experimental FIB micro-milling on cemented carbide have been conducted according to the L₁₆ orthogonal array of Taguchi technique. Beam current, extraction voltage, angle of beam incidence, dwell time and percentage overlap between beam diameters have been considered as process variables of FIB micro-milling in experimental design. Material removal rate (MRR) and surface roughness have been determined experimentally for FIB micro-milling of cemented carbide and beam current has been identified as the most significant parameter. The minimum surface roughness of 5.6 nm has been reported on cemented carbide, which is not a usual practice to achieve on such polycrystalline material, and hence it may be considered as a significant research contribution. Maximum MRR of 0.4836 μm³/s has been reported. Moreover, genetic algorithm toolbox of MATLAB has been utilized for multi-objective optimization between MRR and surface roughness. The corresponding optimum values of MRR and surface roughness for multi-objective optimization have been represented by pareto optimum solution generated by genetic algorithm. The research work presented in this paper determines the setting of process parameters of FIB micro-milling for achieving a specific combination of MRR and surface roughness on cemented carbide.     

Investigation on the effects of ultrasonic vibration on material removal rate and surface roughness in wire electrical discharge turning

An investigation has been made to combine ultrasonic vibration and wire electrical discharge turning. Design of a submerged, precise, flexible, and corrosion-resistant rotary spindle is introduced. The spindle was mounted on a five-axis wire electrical discharge machine to rotate the workpiece in order to generate free-form cylindrical geometries. An auxiliary device that produces ultrasonic vibration was installed between the two wire guides. The ultrasonic system consists of an ultrasonic generator, a transducer, and a wire holder. When the wire is being driven, the transducer together with the wire holder vibrates under the resonance condition. Material removal rate (MRR) indicates efficiency and cost-effectiveness of the process. Experimental results show that wire vibration induced by ultrasonic action has a significant effect on material removal rate. This study has been conducted to evaluate the influence of four design factors: power, pulse off time, spindle rotational speed, and ultrasonic vibration over material removal rate. This has been done by means of design of experiments technique. Analysis of variance was used to determine significant effective factors and also to obtain an equation based on data regression. Experimental results indicate that ultrasonic vibration and power are the most significant influencing parameters on MRR. Rotational speed and pulse off time are the next in ranking. In order to study surface roughness, R _a is measured in different machining parameters.     

Effect of machining parameters on surface roughness and tool wear for 7075 Al alloy SiC composite

In the present study, an attempt has been made to investigate the influence of cutting speed, depth of cut, and feed rate on surface roughness during machining of 7075 Al alloy and 10 wt.% SiC particulate metal-matrix composites. The experiments were conducted on a CNC Turning Machine using tungsten carbide and polycrystalline diamond (PCD) inserts. Surface roughness of 7075Al alloy with 10 wt.% SiC composite during machining by tungsten carbide tool was found to be lower in the feed range of 0.1 to 0.3 mm/rev and depth of cut (DOC) range of 0.5 to 1.5 mm as compared to surface roughness at other process parameters considered. Above cutting speed of 220 m/min surface roughness of SiC composite during machining by PCD tool was less as compared to surface roughness at other values of cutting speed considered. Wear of tungsten carbide and PCD inserts was analyzed using a metallurgical microscope and scanning electron microscope. Flanks wear of carbide tool increased by a factor of 2.4 with the increase of cutting speed from 180 to 240 m/min at a feed of 0.1 mm/rev and a DOC of 0.5 mm. On the other hand, flanks wear of PCD insert increased by only a factor of 1.3 with the increase of cutting speed from 180 to 240 m/min at feed of 0.1 mm/rev and DOC 0.5 mm.     

Theoretical model of brittle material removal fraction related to surface roughness and subsurface damage depth of optical glass during precision grinding

Brittle material removal fraction (BRF) is defined as the area fraction of brittle material removed on machined surface. In the present study, a novel theoretical model of BRF was proposed based on indentation profile caused by intersecting of lateral cracks. The proposed model is related to surface roughness and the subsurface damage (SSD) depth of optical glass during precision grinding. To investigate the indentation profile, indentation tests of K9 optical glass were conducted using single random-shape diamond grains. The experimental results indicate that the indentation profile is an exponent function. To verify the proposed BRF model, BRF, surface roughness and SSD depth of K9 optical glasses were investigated by a series of grinding experiments with different cutting depths. The experimental results show that BRF is dependent on surface roughness and SSD depth. The relationship between BRF, surface roughness and SSD depth is in good accordance with the proposed theoretical model. The proposed BRF model is a reasonable approach for estimating surface roughness and SSD depth during precision grinding of optical glass.     

Modeling and analysis of electrode wear and white layer thickness in die-sinking EDM process through response surface methodology

In this study, an attempt has been made to model electrode wear (EW) and recast layer thickness (WLT) through response surface methodology (RSM) in a die-sinking EDM process. A central composite rotatable design (CCRD) involving three variables with five levels has been employed to establish a mathematical model between input parameters and responses. Pulse on-time, pulse off-time and pulse current were changed during the tests based on the CCRD. The results of analysis of variance (ANOVA) indicated that the proposed mathematical models obtained can adequately describe the performances within the limits of factors being studied. The experimental and predicted values were in a good agreement.     

Prediction of the surface roughness and material removal rate in chemical mechanical polishing of single-crystal SiC via a back-propagation neural network

Chemical mechanical polishing (CMP) is a common method for realising the global planarisation and polishing of single-crystal SiC and other semiconductor substrates. The strong oxidant hydroxyl radicals (·OH) generated by the Fenton reaction can effectively oxidise and corrode the SiC substrate, and are thus used to improve the material removal rate (MRR) and surface roughness (Ra) after polishing of SiC during CMP. Therefore, it is necessary to study the material removal mechanism in detail. Based on the modified Preston equation, the effects of the CMP process parameters on the MRR and Ra after polishing of SiC and their relationship were studied, and a prediction model of the CMP process parameters, MRR, and Ra after polishing was also established based on a back-propagation neural network. The MRR initially increased and then decreased, and the Ra after polishing initially decreased and then increased, with increasing FeSO₄ concentration, H₂O₂ concentration, and pH value. The MRR continuously increased with increasing abrasive particle size, abrasive concentration, polishing pressure, and polishing speed. However, the Ra continuously decreased with increasing abrasive particle size and abrasive concentration, increased with increasing polishing pressure, and initially decreased and then increased with increasing polishing speed. The established prediction model could accurately predict the relationship between the process parameters, MRR and Ra after polishing in CMP (relative prediction error of less than 10%), which could provide a theoretical basis for CMP of SiC.     

Modelling of surface finish and material removal rate in rough honing

In the present work influence of different parameters of the rough honing process on surface roughness and material removal rate were studied. Specifically, second order mathematical models are presented for mean average roughness Ra (μm), maximum peak-to-valley roughness Rt (μm) and material removal rate Qm (cm min⁻¹), obtained by means of regression analysis.     

Electrical discharge machining of the AISI D6 tool steel: Prediction and modeling of the material removal rate and tool wear ratio

In this investigation, response surface method was used to predict and optimize the material removal rate and tool wear ratio during electrical discharge machining of AISI D6 tool steel. Pulse on time, pulse current, and voltage were considered as input process parameters. Furthermore, the analysis of variance was employed for checking the developed model results. The results revealed that higher values of pulse on time resulted in higher values of material removal rate and lower amounts of tool wear ratio. In addition, increasing the pulse current caused to higher amounts of both material removal rate and tool wear ratio. Moreover, the higher the input voltage, the lower the both material removal rate and tool wear ratio. The optimal condition to obtain a maximum of material removal rate and a minimum of tool wear rate was 40 μs, 14 A and 150 V, respectively for the pulse on time, pulse current and input voltage.