Machining Of AISI D2 Steel Research Articles

Optimization of Cutting Parameters for Energy Efficiency in Wire Electrical Discharge Machining of AISI D2 Steel

Optimization of Cutting Parameters for Energy Efficiency in Wire Electrical Discharge Machining of AISI D2 Steel

Solution of Multi-response Optimization: Case Study in turning of AISI D2 Steel using Fuzzy TOPSIS Integrated Taguchi Approach

The present work aims to access the consequence of turning parameter e.g., spindle speed (N), depth of cut (D) and feed rate (f) on several execution indices viz. surface roughness and material removal rate in the machining of AISI D2 steel by means of PVD coated carbide tool. Experimentation trials were carried in accordance to the L9 orthogonal array. In addition, attempts were made to turn the multiple responses got into single responses utilizing TOPSIS. In this study, Decision-Makers’ (DMs’) subjective opinion (in regards of response weight) which were expressed in language-based terminologies has been transformed into fuzzy numbers. Fuzzy representation of multiple-judgments have been aggregated and finally defuzzified to obtain crisp weight. Response data thus obtained from machining experiments along with crisp weight of the responses have been utilized in TOPSIS. At last, for determining the combination of optimal parameters, Taguchi has been implemented.

Experimental Analysis of Canola Oil as Dielectric Fluid in Electric Discharge Machining of AISI D2 Steel

Electric Discharge Machining (EDM) is a thermo-electrical disintegration process, utilized to cut hard materials into complex shapes. In EDM, the three vital components are work-piece material, electrode material and dielectric liquid. The significant function of the dielectric fluid is to produce a breakdown voltage for creating a spark between work-piece and electrode. This spark creates ionization by plasma generation and it expels the material from work-piece. In most cases, hydrocarbon-based dielectric liquid is preferred in the EDM process. This kind of dielectric liquid generates hazardous gases which are harmful to people, environment and ecological balances. In this experiment, Canola oil is utilized and the outcomes are compared. The input parameters are Pulse on/off time, current and gap voltage. Copper, Brass and Tungsten-Copper (Alloy) are used as electrodes. The output values from the experiments are Surface Roughness (SR), Electrode Wear Rate (EWR) and Material Removal Rate (MRR). The outcomes demonstrated that Canola oil-based dielectric fluid has comparable properties of conventional based dielectric fluid. This Canola based dielectric gives more prominent MRR value than EDM oil-based dielectric liquid and furthermore higher EWR and SR values are recorded. The proposed Canola oil-based dielectric fluid is biodegradable and has improved suitability in machining.

INVESTIGATION OF EFFECTS OF DIELECTRIC TYPE AND POLARITY ON ELECTRIC DISCHARGE MACHINING OF AISI D2 STEEL USING RESPONSE SURFACE METHODOLOGY

This paper presents a study on optimisation of process parameters of die-sinking electric discharge machining. The influence of dielectric type, electrode polarity, discharge current and gap on the material removal rate (MRR) and surface roughness for machining of AISI D2 steel have been studied. Response surface methodology suggested in the existing literature was employed for conducting the experiments. Kerosene and transformer oils were found as the best dielectric for MRR and surface roughness, respectively. It was also found that electrode with positive polarity offers high MRR while negative polarity ensures best surface finish. ANOVA results indicated that discharge current was the most influencing factor affecting the performance measures, MRR and surface roughness. Although discharge gap showed low effect on MRR and surface roughness, it was effective for debris removal. Empirical models were developed to optimise the results of MRR and surface roughness.

Performance parameters studies in machining of AISI D2 steel with dot-textured, groove-textured & non-textured cutting tool at the flank face

Surface texturing is a method where there is a change in tribological properties due to change in surface characteristics. The present work is a comparison of the performance of dot-textured coated carbide tool with groove-textured and non-textured coated carbide tool in machining AISI D2 steel. Micro dot textures are made on the flank face using micro-electric discharge machining. Considering full factorial design, total 8 experiments are performed with cutting speed, feed and depth of cut as process parameters. For each parameter, two levels are considered. Flank wear and surface roughness are investigated to compare the function of dot-textured coated carbide tool with the groove-textured and non-textured coated carbide tool. In every condition, flank wear is less in machining with both types of textured tool in comparison to the non-textured coated carbide tool. In most of the cases, surface roughness is less for both types of textured tool than the non-textured tool. Comparing dot-textured tool over groove-textured tool, it is observed that dot-textured tool is better than the groove-textured tool in reducing flank wear and surface roughness. Analysis of white layer and micro hardness at the machined surfaces revealed that dot-texturing helps in minimizing the thickness of white layer and micro-hardness property in comparison to groove-textured and non-textured tool. Thus, dot texturing is found to be better in improving machinability than groove-textured and non-textured tool.

Optimization of process parameters using graphene-based dielectric in electric discharge machining of AISI D2 steel

Hard to machine materials have growing demand in industrial sector especially in nuclear, automotive, and aerospace industries for sustainable production. These materials cannot be machined by typical machining methods or conventional methods, and for machining such materials, nonconventional machining method are usually used. Electric discharge machine is widely used for machining such materials and complex geometries. This research aims to optimize the process parameters while electric discharge machining of AISI D2 steel using nanofluids. The effects of four most influencing factors including pulse-off time, discharge current, pulse-on time, and conc. of nanoparticles have been investigated. Graphene nanoplatelets mixed with kerosene oil were used as a dielectric. Box-Bhenken design based on response surface methodology (RSM) was used for experimentation. Regression models for performance measures such as material removal rate, surface roughness, and white layer thickness have been developed using RSM. ANOVA has been carried out for identifying the most significant factors. Multi-objective optimization has been carried out in terms of desirability function by establishing a compromise between maximum material removal rate and minimum surface roughness and white layer. ANOVA results shows that conc. of nanoparticles is the most significant parameter affecting the performance measures followed by the discharge current. The confirmatory tests were run for verifying and validating the results, and improvements in the performance measures such as MRR, Ra, and WLT up to 21.93 mm3/min, 3.98 μm, and 19.13 μm, respectively, at an optimum have been observed. Multi-response optimization yielded compound desirability of 85.7% for the selected levels of process parameters for machining of AISI D2 steel.

Process Parameters Optimization Using Taguchi’s Orthogonal Array and Grey Relational Analysis during Hard Turning of AISI D2 Steel in Forced Air-Cooled Condition

Optimization of process parameters play an important role in machining operations. The present work deals with a multi-objective optimization method (Grey relational analysis) and Taguchi’s orthogonal array (OA) technique in machining of AISI D2 steel using coated carbide tool in forced air-cooled condition considering it to be environment friendly. The process parameters considered are cutting speed, feed and depth of cut having three levels each. The performance parameters are surface roughness, flank wear and cutting force. Taguchi’s orthogonal array L9 is considered for the experimental design. Grey relational analysis technique is used for finding the optimal settings of the process parameters to obtain the value of surface roughness, flank wear and cutting force. The optimum grey relational grade has been found out to get the optimal setting of the experimental run. Then level wise grey relational grade has been found out from the experimental run for each parameter. The optimal settings based on the rank of the grey relational grade in the experimental run and level wise optimal settings of the parameters from the grey relational grade have been compared. The improvement of performance parameters is confirmed from the grey relational prediction analysis in comparison to optimal values in the experimental run.

Investigating the effects of electric discharge machining parameters on material removal rate and surface roughness on AISI D2 steel using RSM-GRA integrated approach

The present research focuses on optimizing the process parameters of die-sinking electric discharge machining on tool steel. The basic objective of this research is to investigate the influence of two categorical factors including dielectric type and electrode polarity, and two numeric factors including discharge current, and Spark/Discharge Gap on material removal rate (MRR) and surface roughness (Ra) for machining of AISI D2 steel. Box-Bhenken design based on response surface methodology (RSM) was applied for experimental design. For estimation and evaluation, the effects of the process parameters on response variables, RSM has been integrated with grey relational analysis (GRA). Ranking of factors has been done with respect to the grey relational grade. (ANOVA) was further performed for determining the significance of grey relational grade. ANOVA results reveal that that polarity having 50% of percentage contribution was the most significant factor affecting the performance measures followed by the spark gap, discharge current, and dielectric type. The grey relational grades were further optimized through desirability function and the optimal condition for input parameters was obtained. The optimum levels were discharge current at 15 A, dielectric type of kerosene oil, spark gap at 6 mm, and polarity of positive has been determined. The confirmatory tests were run for verifying and validating the results and improvement in productivity (MRR) up to 17.23 mm3/min and quality (Ra) up to 3.86 μm at an optimum have been observed.

A comparative study in machining of AISI D2 steel using textured and non-textured coated carbide tool at the flank face

Surface texturing over a cutting tool in machining is a process where a tribological alteration occurs in the tool-chip interfaces. The present work analyzes the influence of surface texturing of the coated carbide tool on the flank face during machining. Linear microgrooves are prepared on the flank face of the coated carbide tool orthogonal to the cutting edge. The experiments are conducted in the dry turning of AISI D2 steel (45 HRC) using both textured and non-textured tool. Total eight experiments are conducted considering three factors (cutting speed, feed, and depth of cut) having two levels each. The responses of flank wear and surface roughness are studied for comparison of the performance of textured tool with the non-textured tool. In all the cases, flank wear of the textured tool is found to be less as compared to the non-textured tool. Other hand, except few in most of the cases, surface roughness is found to be lower with textured tool as compared to non-textured tool. The analysis of machined samples revealed that texturing helps in reducing the formation of white layer thickness and micro-hardness as compared to the non-textured tool. The decrease in white layer thickness at the machined surface is supported by the decrease in temperature at the tool-chip interface using the textured tool as compared to the non-textured tool. Thus, texturing in flank face of coated carbide tool improves the machinability in terms of flank wear and tool life.

Machining performance optimization during plasma arc cutting of AISI D2 steel: application of FIS, nonlinear regression and JAYA optimization algorithm

The work focuses on assessing the optimal machining conditions which could simultaneously satisfy multiple process performance indices during machining of AISI D2 steel. The main characteristic indices that have been considered here for evaluating plasma arc machining are surface roughness and material removal rate; the corresponding machining parameters are cutting speed, gas pressure and torch height. The study proposes an integrated optimization module combining fuzzy inference system, nonlinear regression and JAYA algorithm towards optimizing correlated multi-response features during machining of AISI D2 steel. Optimum value of machining parameters found as cutting speed of 4000 m/min, gas pressure of 95 psi and torch height of 0.5 mm using aforementioned methodology. Application potential of the aforesaid integrated optimization route has been compared to that of teaching–learning based optimization (TLBO) algorithm and genetic algorithm. It has been concluded that JAYA algorithm possesses less convergence time and hence execution is faster as compared to TLBO and genetic algorithm.

Investigation of Surface Roughness, Kerf Width and MRR on AISI D2 Steel Machined by Wire EDM

Engineering industry has made tremendous growth in its manufacturing fields in the last decade but still the modern machines are not utilized up to the desired level. Wire electrical discharge machining (WEDM) is one such sophisticated machining process used in industry, which is a high-speed cutting and high-precision machining to realize productivity and improved accuracy for all manufacturing parts. Optimisation of the operating parameters of WEDM will result in efficiency with regard to higher surface finish and maximum material removal rate. The present investigation of the cutting performance on die steel with different parameters such as pulse on time, pulse off time, servo voltage, wire feed, current and cutting speed were involved to determine by which using wire electric discharge machining(WEDM) process. The shape of the wire and base material dimensions of brass wire 0.25mm diameter and AISI D2 steel with 100× 100 × 10 mm were carried out. The cutting performance outputs considered in this study are material removal rate (MRR), Kerf width and surface roughness (Ra). Experiments were carried out under Taguchi's L16 orthogonal array under different conditions of parameters. An optimal combination of WEDM parameters for proper machining of AISI D2 steel to achieve better surface roughness and Materials Removal Rate (MRR) is achieved in this investigation.

Parametric optimization of MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using Taguchi-based utility approach

This paper reports the effect of process parameters on material removal rate (MRR) and surface roughness (Ra) in wire electro discharge machining of AISI D2 steel. The wire electro discharge machining characteristics of AISI D2 steel have been investigated using an orthogonal array of design. The pulse on time and servo voltage are the most significant parameter affecting MRR and surface roughness during WEDM process. The simultaneous performance characteristics MRR and surface roughness was optimized by Taguchi based utility approach. The machined surface hardness is higher than the bulk material hardness due to the repetitive quenching effect and contained various oxides in the surface recast layer. The experiments were performed by different cutting conditions of pulse on time (T on ), pulse off time (T off ), servo voltage (SV) and wire feed (WF) by keeping work piece thickness constant. Taguchi L27 orthogonal array of experimental design is employed to conduct the experiments. Multi-objective optimization was performed using Taguchi based utility approach to optimize MRR and Ra. Analysis of means and variance on to signal to noise ratio was performed for determining the optimal parameters. It reveals that the combination of Ton3, Toff1, SV1, WF2 parameter levels is beneficial for maximizing the MRR and minimizing the Ra simultaneously. The results indicated that the pulse on time is the most significant parameter affects the MRR and Ra. The melted droplets, solidified debris around the craters, cracks and blow holes were observed on the machined surface for a higher pulse on time and lower servo voltage. Recast layer thickness increased with an increase in pulse on time duration. The machined surface hardness of D2 steel is increased due to the repetitive quenching effect and formation oxides on the machined surface.

Employing Ti nano-powder dielectric to enhance surface characteristics in electrical discharge machining of AISI D2 steel

Manufacturing components with superior surface characteristics is challenging when electrical discharge machining (EDM) is employed for mass production. The aim of this research is to enhance the characteristics of AISI D2 steel surface machined with EDM through adding Ti nano-powder to dielectric under various machining parameters, including discharge duration (Ton) and peak current (I). Surface roughness profilometer, FESEM and AFM analysis were utilized to reveal the machined surface characteristics in terms of surface roughness, surface morphology and surface micro-defects. Moreover, EDX analysis was performed in order to evaluate the atomic deposition of Ti nano-powder on the surface. The concentration of Ti nano-powder in dielectric was also examined using ESEM and EDX. According to the results, the addition of Ti nano-powder to dielectric notably enhanced the surface morphology and surface roughness at all machining parameters except Ton=340μs. Of these parameters, maximum enhancement was observed at Ton=210μs, where the material removal rate and average surface roughness improved by ∼69 and ∼35% for peak current of 6 and 12A, respectively. Elemental analysis signified negligible Ti deposition on the machined surface while the atomic concentration of Ti was increased around the crack areas.

Thermal Energy Distribution and Optimization of Process Parameters during Electrical Discharge Machining Of AISI D2 Steel

The apportionment of energy between tool and workpiece of EDM is of paramount importance since it dictates all the characteristics or performance of EDM process. Our current study attempts to determine distribution of energy to tool and workpiece during EDM of AISI D2 steel using copper electrode. The influence of most significant EDM parameters like discharge current (Ip) and pulse duration (Ton) on the energy distribution was studied. Taguchi method consisting of an L9 orthogonal array was selected to study the effects of the various parameters on the output responses. ANOVA method was used to analyse the effect of input parameters on the energy transferred to the workpiece and tool and find the optimal process parameters. The experimental results revealed that the discharge current (Ip) was the dominant factor in determining the percentage energy responsible for material removal rate (MRR) and the pulse duration was the dominant factor in determining the percentage energy responsible for tool wear rate (TWR).

Optimization of WEDM Parameters Using Taguchi Technique and Response Surface Methodology in Machining of AISI D2 Steel

The objective of the present work is to investigate the effects of various WEDM process parameters such as pulse on time, pulse off time, servo voltage and wire feed rate on the Material Removal Rate (MRR), Surface Roughness (SR) and cutting rate. Secondly, to obtain the optimal settings of machining parameters at which the Material Removal Rate (MRR) and cutting rate are maximum and the Surface Roughness (SR) is minimum in a range. The experiments were carried out as per design of experiment approach using L27 (34) orthogonal array. In the present investigation, AISI D2 steel specimen is machined by using brass wire as electrode and the response surface methodology (RSM) is used for modelling a second-order response surface to estimate the optimum machining condition to produce the best possible response within the experimental constraints. The results from this study will be useful for manufacturing engineers to select appropriate set of process parameters to machine AISI D2 steel.

A DEA Approach for Optimization of Multiple Responses in Electrical Discharge Machining of AISI D2 Steel

Abstract Present research proposes an optimization methodology for the selection of best process parameters in multi-response situation. Experiments have been conducted on a die-sinking electric discharge machine under different conditions of process parameters. A response surface methodology (RSM) is adopted to establish effect of various process parameters such as discharge current (Ip), pulse on time (Ton), duty factor (t) and flushing pressure (Fp) on four important responses like material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra) and circularity (r1/r2) of machined component. Since the natures of responses are contradicting in nature, it is difficult to find a single combination of machining parameters that provides the best performance satisfying all responses simultaneously. In order to achieve best machining condition, an equivalent single response capable of representing all individual responses is needed. The work includes data envelopment analysis (DEA) to obtain relative efficiency for each experimental run treating as decision making unit (DMU). Each DMU is evaluated using LINGO software to obtain relative efficiency. The relative efficiency is ranked in ascending order and average ranked value (ARV) is calculated to find the optimal solution. Finally, the optimal setting capable of improving all the responses simultaneously is found to be Ip=7 amp, Ton= 200 |a.s, t = 90%, and Fp = 0.4 kg/cm 2 . With this best combination of factorial level, the experimental values of responses are obtained as MRR=13.9600 mnrVmin, TWR=0.0201 mnrVmin, Ra=4.9300 umandcircularity=0.8401.

Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI D2 steel

In the present research, two neuro-fuzzy models and a neural network model are presented for predictions of material removal rate (MRR), tool wear rate (TWR), and radial overcut (G) in die sinking electrical discharge machining (EDM) process for American Iron and Steel Institute D2 tool steel with copper electrode. The discharge current (I p), pulse duration (T on), duty cycle (τ), and voltage (V) are considered as inputs to the network. A full-factorial design was used to conduct the experiments with various levels of I p, T on, τ, and V. The analysis of variance results reveal that I p is the most influencing factor for MRR and G, having the highest degree of contributions of 87.61% and 81.90%, respectively. In case of TWR, T on has the highest degree of contribution of 46.05% and is the most significant factor. The half of the experimental data set was used to train the networks and was tested for convergence with a different set of data to obtain appropriate number of neurons, epoch, and the fuzzy rule base. The mean square error convergence criteria, both in training and testing, came out very well. The developed models are found to approximate the responses quite accurately. Moreover, the predicted results based on above models have been confirmed with unseen validation set of experiments and are found to be in good agreement with the experimental results. The comparison results reveal that the artificial neural network and the neuro-fuzzy models are comparable in terms of accuracy and speed, and further, the proposed models can be employed successfully in prediction of MRR, TWR, and G of the stochastic and complex EDM process.