Modeling and analysis of the effects of machining parameters on the performance characteristics in the EDM process of Al2O3+TiC mixed ceramic

Electric discharge machining (EDM) has achieved remarkable success in the manufacture of conductive ceramic materials for the modern metal industry. Mathematical models are proposed for the modeling and analysis of the effects of machining parameters on the performance characteristics in the EDM process of Al₂O₃+TiC mixed ceramic which are developed using the response surface methodology (RSM) to explain the influences of four machining parameters (the discharge current, pulse on time, duty factor and open discharge voltage) on the performance characteristics of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The experiment plan adopts the centered central composite design (CCD). The separable influence of individual machining parameters and the interaction between these parameters are also investigated by using analysis of variance (ANOVA). This study highlights the development of mathematical models for investigating the influences of machining parameters on performance characteristics and the proposed mathematical models in this study have proven to fit and predict values of performance characteristics close to those readings recorded experimentally with a 95% confidence interval. Results show that the main two significant factors on the value of the material removal rate (MRR) are the discharge current and the duty factor. The discharge current and the pulse on time also have statistical significance on both the value of the electrode wear ratio (EWR) and the surface roughness (SR).     

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.     

Optimizing machining parameters of silicon carbide ceramics with ED milling and mechanical grinding combined process

A novel combined process of machining silicon carbide (SiC) ceramics with electrical discharge milling and mechanical grinding is presented. The process is able to effectively machine a large surface area on SiC ceramics with a good surface quality. The effect of tool polarity on the process performance has been investigated. The effects of peak current, peak voltage, pulse on-time and pulse off-time on the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR) have been investigated with Taguchi experimental design. The mathematical models for the MRR, EWR, and SR have been established with the stepwise regression method. The experiment results show that the MRR, EWR, and SR can reach 46.2543 mm³/min, 20.7176%, and 0.0340 µm, respectively, with each optimal combination level of machining parameters.     

An experimental investigation on machining parameters of AISI D2 steel using WEDM

The performance of the wire electrodischarge machining (WEDM) machining process largely depends upon the selection of the appropriate machining variables. Optimization is one of the techniques used in manufacturing sectors to arrive for the best manufacturing conditions, which are essential for industries toward manufacturing of quality products at lowest cost. As there are many process variables involved in the WEDM machining process, it is difficult to choose a proper combination of these process variables in order to maximize material removal rate and to minimize tool wear and surface roughness. The objective of the this work is to investigate the effects of process variables like pulse on time, pulse off time, peak current, servo voltage, and wire feed on material removal rate (MRR), surface roughness (SR), gap voltage, gap current, and cutting rate in the WEDM machining process. The experiment has been done using Taguchi’s orthogonal array L27 (3⁵). Each experiment was conducted under different conditions of input parameters and statistically evaluated the experimental data by analysis of variance (ANOVA) using MINITAB and Design Expert tools. The present work also aims to develop mathematical models for correlating the inter-relationships of various WEDM machining parameters and performance parameters of machining on AISI D2 steel material using response surface methodology (RSM).The significant machining parameters and the optimal combination levels of machining parameters associated with performance parameters were also drawn. The observed optimal process parameter settings based on composite desirability (61.4 %) are pulse on time 112.66 μs, pulse off time 45 μs, spark gap voltage 46.95 V, wire feed 2 mm/min, peak current of 99.99 A for achieving maximum MRR, gap current, gap voltage, cutting rate, and minimum SR; finally, the results were experimentally verified.     

Experimental study of rotary magnetic field-assisted dry EDM with ultrasonic vibration of workpiece

Dry electrical discharge machining (EDM) is a green machining method which replaces the gas instead of liquid as dielectric medium. Due to the environmentally friendly nature of this method, recently, researchers focused on characterization of this process. In this work, effects of rotary magnetic field and also ultrasonic vibration of workpiece were studied on dry EDM process performance. Conducted experiments were divided in two main stages. At first stage, preliminary experiments were carried out to determine the best tool design in material and geometry points of view by considering the material removal rate (MRR). Also, effect of magnetic field was studied in the first stage. Results of the first stage of experiments indicated that the brass tool with two eccentric holes has the highest MRR rather than the other existing tool. In the second stage of experiments, parametric study on dry EDM process were implemented by using a brass tool with two eccentric holes and by applying rotary magnetic field for all experiments of the second stage. Influences of parameters such as pulse current, pulse on-time, pulse off-time, tool rotational speed, air injection inlet pressure, and especially power of ultrasonic table were studied on MRR, surface roughness (SR), electrode wear rate (EWR), and overcut (OC). Results showed that magnetic field has positive effects on MRR and SR. Also, by application of ultrasonic vibration achieving to superior MRR is feasible. At the end of the work, mathematical models were developed to correlate a relationship between process inputs and main outputs.     

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.     

Experimental modeling and optimization of electric discharge diamond face grinding of metal matrix composite

Machining of metal matrix composites (MMCs) has been a big challenge for manufacturing industries due to its superior mechanical properties. Unconventional machining methods have become an alternative to give desired shapes with intricate profiles and stringent design requirements. The present research investigates the grinding performance of copper–iron–graphite MMC using electric discharge diamond face grinding (EDDFG), which is electric discharge machining-based hybrid machining process. Experiments have been performed on a self-developed experimental setup of EDDFG with scientifically designed experiments. Effects of process input parameters on two important performances, material removal rate (MRR) and surface roughness (SR), have been analyzed. Genetic algorithm-based optimization of MRR and SR models show considerable improvements in both characteristics, as confirmed by verification experiments. Results reveal that peak current is a common significant factor for both MRR and SR.     

Effect on crystalline structure of AISI M2 steel using tungsten–thorium electrode through MRR,EWR, and surface finish

To investigate on the crystalline structure of AISI M2 steel by using tungsten–thorium electrode in electrical discharge machining (EDM) process was studied. Furthermore, the investigation were carried out for finding the value of material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) of tool steel material depending upon three variable input process parameters. On the basis of weight loss, the value of MRR and EWR were calculated at optimized process parameter. Subsequently, surface topography of the processed material were examined through different characterization techniques like scanning electron microscopy (SEM), Optical surface profiler (OSP) and Atomic force microscopy (AFM), respectively. In XRD study, broadening of the peak was observed which confirmed the change in material properties due to the homogeneous dispersion of the particles inside the matrix. Lowest surface roughness and MRR of 0.001208 mg/min was obtained. Minimum surface roughness was obtained 1.12 μm and 2.18427 nm by OSP and AFM study, respectively. Also, minimum EWR was found as 0.013986 mg/min.     

Optimization of machining parameters for EDM operations based on central composite design and desirability approach

A novel aluminium metal matrix composite reinforced with SiC particles were prepared by liquid metallurgy route. Recent developments in composites are not only focused on the improvement of mechanical properties, but also on machinability for difficult-to-machine shapes. Electrical discharge machining (EDM) was employed to machine MMC with copper electrode. using EDM. Experiments were conducted using pulse current, gap voltage, pulse on time and pulse off time as typical process parameters. The experiment plan adopts face centered central composite design of response surface methodology. Analysis of variance was applied to investigate the influence of process parameters and their interactions viz., pulse current, gap voltage, pulse on time and pulse off time on material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). The objective was to identify the significant process parameters that affect the output characteristics. Further a mathematical model has been formulated by applying response surface method in order to estimate the machining characteristics such as MRR, EWR and SR.     

Parametric analysis on electrochemical discharge machining of silicon nitride ceramics

The electrochemical discharge machining (ECDM) process has a potential in the machining of silicon nitride ceramics. This paper describes the development of a second order, non-linear mathematical model for establishing the relationship among machining parameters, such as applied voltage, electrolyte concentration and inter-electrode gap, with the dominant machining process criteria, namely material removal rate (MRR), radial overcut (ROC) and thickness of heat affected zone (HAZ), during an ECDM operation on silicon nitride. The model is developed based on response surface methodology (RSM) using the relevant experimental data, which are obtained during an ECDM micro-drilling operation on silicon nitride ceramics. We also offer an analysis of variance (ANOVA) and a confirmation test to verify the fit and adequacy of the developed mathematical models. From the parametric analyses based on mathematical modelling, it can be recommended that applied voltage has more significant effects on MRR, ROC and HAZ thickness during ECDM micro-drilling operation as compared to other machining parameters such as electrolyte concentration and inter-electrode gap.     

Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology

A study on the radial-mode abrasive waterjet turning (AWJT) of 96 % alumina ceramic is presented and discussed. An experimental investigation is carried out to explore the influence of process parameters (including water pressure, jet feed speed, abrasive mass flow rate, surface speed, and nozzle tilted angle) on the material removal rate (MRR) when turning 96 % alumina ceramic. The experiments are conducted on the basis of response surface methodology (RSM) and sequential approach using face-centered central composite design. The quadratic model of RSM associated with the sequential approximation optimization (SAO) method is used to find optimum values of process parameters in terms of surface roughness and MRR. The results show that the MRR is influenced principally by the water pressure P and the next is abrasive mass flow rate m _a . The optimization results show that the MRR can be improved without increasing the surface roughness when machining 96 % alumina ceramic in the radial-mode abrasive waterjet turning process.     

Investigation into electrochemical micromachining (EMM) through response surface methodology based approach

Electrochemical micromachining (EMM) could be used as one the best micromachining technique for machining electrically conducting, tough and difficult to machine material with appropriate machining parameters combination. This paper attempts to establish a comprehensive mathematical model for correlating the interactive and higher-order influences of various machining parameters, i.e. machining voltage pulse on/off ratio, machining voltage, electrolyte concentration, voltage frequency and tool vibration frequency on the predominant micromachining criteria, i.e. the material removal rate and the radial overcut through response surface methodology (RSM), utilizing relevant experimental data as obtained through experimentation. Validity and correctiveness of the developed mathematical models have also been tested through analysis of variance. Optimal combination of these predominant micromachining process parameters is obtained from these mathematical models for higher machining rate with acuuracy. Considering MRR and ROC simultaneously optimum values of predominant process parameters have been obtained as; pulse on/off ratio, 1.0, machining voltage, 3 V, electrolyte concentration, 15 g/l, voltage frequency of 42.118 Hz and tool vibration frequency as 300 Hz. The effects of various process parameters on the machining rate and radial overcut are also highlighted through different response surface graphs. Condition of machined micro-holes are also exhibited through the SEM micrographs in this paper. Pulse voltage pattern during electrochemical micromachining process has been analyzed with the help of voltage graphs. Irregularities in the nature of pulse voltage pattern during electrochemical micromachining have been observed and the causes of these irregularities are further investigated.     

Application of response surface method on machining of Al–SiC nano-composites

The newly fabricated metal matrix nano-composite (MMNC) of Al 7075 reinforced with 1.5 wt% SiC nano-particles was prepared by a novel ultrasonic cavitation method. The high resolution scanning electron micrograph (SEM) and field emission scanning electron micrograph (FESEM) shows uniform distribution and good dispersion of the SiC nanoparticles within the aluminum metal matrix. Electrical discharge machining (EDM) was employed to machine MMNC with copper electrode by adopting face centered central composite design of response surface methodology. Analysis of variance was applied to investigate the influence of process parameters and their interactions. Further a mathematical model has been formulated in order to estimate the machining characteristics. It has been observed that pulse current was found to be the most important factor affecting all the three output parameters such as material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR). The optimum parameter of combination setting has been identified for the MMNC are voltage 50.00 V, pulse current 8.00 A, Pulse on time 8.00 μs and pulse off time 9.00 μs. Finally the parameters were optimized for maximizing MRR, minimizing EWR and SR using desirability function approach.     

Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis

Electro-discharge machining (EDM) is an enormously used nonconventional process for removing material in die making, aerospace, and automobile industries. It consists of limitations like poor volumetric material removal rate (MRR) and reduced surface quality. Powder mixed EDM (PMEDM) is a new development in EDM to enhance its machining capabilities. The present work investigates the effect of powder concentration (C_p), peak current (I_p), pulse on time (T_on), duty cycle (DC) and gap voltage (V_g) on MRR, tool wear rate (TWR), electrode wear ratio (EWR), and surface roughness (SR) simultaneously for H-11 die steel using SiC powder. Taguchi's L₂₇ orthogonal array has been used to conduct the experiments. Multiobjective optimization using grey relational analysis (GRA) and technique for order of preference by similarity to ideal solution (TOPSIS) has been used to maximize the MRR and minimize the TWR, EWR, and SR and determine the optimal set of process parameters. Analysis of variance (ANOVA) has been performed to understand the significance of each process parameter. Results were verified by conducting confirmatory tests. GRA and TOPSIS exhibit an improvement of 0.1843 and 0.14308 in the preference values, respectively. Microstructure analysis has been done using scanning electron microscope (SEM) for the optimum set of parameters.     

Grinding-aided electrochemical discharge machining of particulate reinforced metal matrix composites

A grinding-aided electrochemical discharge machining (G-ECDM) process has been developed to improve the performance of the conventional ECDM process in machining particulate reinforced metal matrix composites (MMCs). The G-ECDM process functions under a combined action of electrochemical dissolution, spark erosion, and direct mechanical grinding. The tool electrode has a coating containing a hard reinforcement phase of diamond particles. The MMC employed in this study was Al₂O₃ particulate reinforced aluminum 6061 alloy. The material removal mechanism of this hybrid process has been analyzed. The results showed that the grinding action can effectively remove re-cast material deposited on the machining surface. The surface roughness (R _a) measured for the G-ECDM specimen was ten times smaller than that of the specimen machined without grinding aid (i.e., ECDM alone). Moreover, the material removal rate (MRR) of G-ECDM was about three times higher than that of ECDM under the experimental conditions of this study. The voltage waveform and crater distribution were also analyzed, and the experimental results showed that the G-ECDM process operates in a stable condition. The relative importance of the various processing parameters on MRR was established using orthogonal analysis. The results showed that MRR is influenced by the machining parameters in the order of duty cycle?>?current?>?electrolyte concentration. This study showed that the G-ECDM process is superior to the ECDM process for machining particulate reinforced MMCs, where a higher machining efficiency and a better surface quality can be obtained.     

Study of electrochemical machining characteristics of Al/SiCp composites

Non-conventional machining is increasing in importance due to some of the specific advantages which can be exploited during machining operation. Electrochemical machining (ECM) appears to be a promising technique, since in many areas of application, it offers several special advantages including higher machining rate, better precision and control, and a wider range of materials that can be machined. The present work is, therefore, initiated to investigate the influence of some predominant electrochemical process parameters such as applied voltage, electrolyte concentration, electrolyte flow rate and tool feed rate on the metal removal rate (MRR), and surface roughness (Ra) to fulfill the effective utilization of electrochemical machining of LM25 Al/10%SiC composites produced through stir casting. The contour plots are generated to study the effect of process parameters as well as their interactions. The process parameters are optimized based on Response Surface Methodology (RSM).     

Experimental investigation on the electrochemical machining of 00Cr12Ni9Mo4Cu2 material and multi-objective parameters optimization

Special stainless steel 00Cr12Ni9Mo4Cu2 has multiple composition and inhomogeneous tissues; short circuiting will frequently occur when using conventional electrolyte processing. This article analyzes the reason why the process of machining is difficult from the material composition and structure. We used the NaNO₃ and NaClO₃ electrolyte composite to select the appropriate concentration, and then by using the orthogonal experiment and gray relational analysis method, we discussed how the voltage, feed speed, and electrolyte pressure solved the problem of the material removal rate (MRR), surface roughness (SR), and side gap. Under optimal conditions of 20 V, an electrolyte composite concentration of 178 g/l NaNO₃ and 41 g/l NaClO₃, a feed rate of 0.7 mm/min, and an electrolyte pressure of 0.8 MPa, a material removal rate of 100.8 mm3/min, a surface roughness of Ra 0.8 μm, and a side gap of 0.16 mm were produced. Given the same voltage, with an increasing cathode feed rate, the MRR was shown to increase while the surface roughness value and the side gap decreased. Under the same cathode feed rate, the MRR decreases, while the side gap and the surface roughness increase as the electrochemical machining application voltage increases. This study proves that using a certain concentration of electrolyte composite is a simple, low-cost, and feasible approach in improving efficiency and quality.     

Influence of parameters and optimization of EDM performance measures on MDN 300 steel using Taguchi method

Maraging steel (MDN 300) exhibits high levels of strength and hardness. Optimization of performance measures is essential for effective machining. In this paper, Taguchi method, used to determine the influence of process parameters and optimization of electrical discharge machining (EDM) performance measures on MDN 300 steel, has been discussed. The process performance criteria such as material removal rate (MRR), tool wear rate (TWR), relative wear ratio (RWR), and surface roughness (SR) were evaluated. Discharge current, pulse on time, and pulse off time have been considered the main factors affecting EDM performance. The results of the present work reveal that the optimal level of the factors for SR and TWR are same but differs from the optimum levels of the factors for MRR and RWR. Further, discharge current, pulse on time, and pulse off time have been found to play a significant role in EDM operations. Detailed analysis of structural features of machined surface was done by using scanning electron microscope (SEM) to understand the influence of parameters. SEM of electrical discharge machining surface indicates that at higher discharge current and longer pulse on duration give rougher surface with more craters, globules of debris, pockmarks or chimneys, and microcracks than that of lower discharge current and lower pulse on duration.     

Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite

This paper reports on an experimental investigation of small deep hole drilling of Inconel 718 using the EDM process. The parameters such as peak current, pulse on-time, duty factor and electrode speed were chosen to study the machining characteristics. An electrolytic copper tube of 3 mm diameter was selected as a tool electrode. The experiments were planned using central composite design (CCD) procedure. The output responses measured were material removal rate (MRR) and depth averaged surface roughness (DASR). Mathematical models were derived for the above responses using response surface methodology (RSM). The results revealed that MRR is more influenced by peak current, duty factor and electrode rotation, whereas DASR is strongly influenced by peak current and pulse on-time. Finally, the parameters were optimized for maximum MRR with the desired surface roughness value using desirability function approach.     

超声磁力复合研磨钛合金锥孔的试验研究

为了提高钛合金锥孔的研磨质量和研磨效率,提出了采用超声波振动辅助磁力研磨的复合加工方案。加工时,磨粒在磁场束缚下切削锥孔表面,并对其进行不断撞击,且因为磁场力、超声振动力和离心力等综合影响的原因,磨粒的切削轨迹呈现明显的多向性。针对钛合金锥孔,与传统磁力研磨法进行试验对比,并分析研磨后试件的材料去除量、表面粗糙度和表面形貌等来验证超声磁力复合研磨的效果。结果表明：超声磁力复合研磨加工效率得到提高;锥孔的材料去除量增加至1.6倍;研磨后锥孔平均表面粗糙度由原始的R_a1.23 μm降至R_a0.25 μm,下降率是传统工艺的1.3倍;试件表面的微波峰、凹坑和加工纹理均被去除,锥孔表面质量得到显著提高,且试件形状精度得到改善。