Surface Roughness In Electrical Discharge Machining Research Articles

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining

In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Valext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (Cp) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (Sc). However, the highest % significance in SR is given by Sc (36.86 %), followed by Cp (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.

Effect of pulse current in manufacturing of cardiovascular stent using EDM die-sinking

This paper proves that die-sinking electrical discharge machining (EDM) is an alternative and attractive manufacturing method of producing cardiovascular stent. This study investigates the effect of pulse currents on the material removal rate (MRR), the surface of EDM, and the upper surface of the strut stent. The observed surface characteristics of the strut were surface roughness, recast layer, and the diameter of the crater. The recast layer and crater were analyzed by an optical microscope, and the surface roughness was measured by a surface profilometer. The pulse currents which were used in the manufacturing of stent by die-sinking EDM were 1.5 A/45 μs, 3.0 A/90 μs, 4.5 A/120 μs, and 6 A/150 μs. The experiment shows that the stent is successfully manufactured from minitube AISI 316L by die-sinking EDM. The diameter, length, and thickness of the stent are 4 mm, 20 mm, and 0.3 mm, respectively. The results show that increasing of pulse current will cause the increase of MRR, the thickness of recast layer, the surface roughness, and the crater diameter. The MRR, thickness of the recast layer, surface roughness, and crater diameter are also influenced by the pulse on time. The EDM surface roughness, the average thickness of recast layer, and the diameter of crater are in the range of 3.49–9.53 μm, 17.6–97 μm, and 75.84–276.5 μm, respectively, and the upper surface roughness of the strut is 0.5534 μm. The best quality of the strut is generated from the pulse current 1.5 A/45 μs.

Assessment of Surface Roughness in EDM of RHA, Cu and Mg Reinforced Hybrid MMC using L27 Taguchi’s Orthogonal Array Technique

The goal of this research is to develop Al alloy 6061 matrix composites of lower cost with higher performance with the using agricultural waste Rice Husk Ash (RHA) as a major reinforcement. The weight percentage of RHA at 6,8 and 10 % is used in the basic matrix of Al6061 alloy. The copper (Cu) and magnesium (Mg) are kept at constant level i.e 3% and 1 % of weight(wt) respectively. The Al-matrix hybrid composites have been developed by stir casting method. The investigation has been made to explore the effect of different weight percentage of RHA reinforced hybrid composite and other machining factors on Surface Roughness(SR) for machining of Al matrix hybrid composites using electric discharge machining (EDM) by Taguchi’s approach. An L27 Orthogonal Array (OA) for 6 different factors each at 3 stages, has been selected to perform the experiments. Optimal combination of SR-related parameters of machining and the significant parameters has been highlighted. Machined surfaces have been characterised by SEM micrographs

Optimization of surface roughness in EDM of pure magnesium (Mg) using TLBO

Magnesium and its alloys can also be used as a degradable implant as a ceramic material which is highly applicable in biomedical industries. As medical devices require a strong surface integrity of the parts as well as high geometric precision. Therefore, it is necessary to find the optimal surface parameters in order to minimize the risk of inflammatory reactions, reduce friction or increase cell adhesion for optimal fitting, mechanical stability, and biocompatibility. Hence, present work emphasized on the effect of input parameters such as distributor current (A), work on time (B), work off time (C) on surface roughness during the Electric Discharge Machining (EDM) of pure magnesium (Mg). In the present work, Taguchi and TLBO analysis are used to obtain the optimal parametric combination to achieve better surface quality.

The Effect of Electropolishing Time Variation at Room Temperature and Low Voltage on the Surface Quality of Cardiovascular Stent

Electropolishing is an attractive method for surface smoothing of cardiovascular stent. This study investigated the effect of times of electropolishing on the surface characteristics both are upper surface and surface of the strut of cardiovascular stent after the by die sinking electrical discharge machining (EDM). The observed surface characteristics of the strut were recast layer, surface roughness and brightness. The weight analysis, and the reduction of the width strut were conducted. The recast layer was analyzed by optical microscope qualitatively, the surface roughness was measured by surface texture measuring instrument, the weight analysis and the reduction of width strut were calculated. The stent was made from steel AISI 316 L. The times which were used in the electropolishing were 3 minutes, 7 minutes, and 11 minutes. The experimental results show that the time for smoothing and brightening of stent at room temperature and low voltage 5 V is 7 minutes. The times affect the upper and EDM surface roughness, the weight of stent and the width of strut. The results show that increasing of times, than the value of surface roughness, the weight of stent and the width of strut will decrease, and vice versa. The average surface roughness of EDM surface after electropolishing is in the range of 3.49 – 1.62 µm. The average surface roughness of upper surface after electropolishing is in the range of 0.55-0.22 µm. The weight analysis show that the loss of weight is in the range of 0.12-1.12 %, and the reduction of width strut is in the range of 11.02 – 69.3 %.

Experiment investigation of using wire electrochemical machining in deionized water to reduce the wire electrical discharge machining surface roughness

When the workpiece is sliced by using wire electrical discharge machining (WEDM), the materials are removed by melting and evaporation. Owing to the material removal mechanism, the surface of the workpiece after WEDM is composed of recast layer and numerous discharge craters, leading to the low surface roughness. In this study, wire electrochemical machining (WECM) is introduced to eliminate the recast layer and improve the surface quality of the workpiece cut by using WEDM. Two methods, which are based on electrochemical dissolution reactions, are proposed to dissolve the recast layer and craters on the WEDM surface. The processes are conducted on the same machine tool with the same electrolyte (deionized water) and the same machining parameters. Two factors, which have a great influence on the surface roughness, namely the feed rate of the wire electrode and the movement distance of the workpiece, are analyzed. Experiment results show that the recast layer and craters on the WEDM surface can be dissolved owing to the anodic dissolution of WECM, and the surface quality can be improved. In order to obtain the good surface roughness, the wire electrode should be fed as slow as possible during the electrolysis, and the movement distance of the workpiece should be appropriate.

Development of Hybrid Modeling and Prediction of SR in EDM of AISI1020 Steel Material Using ANFIS

Background/Objectives: Surface roughness plays a major role in determining how an original component will interact with its environment. For getting a good surface finish industries spent huge cost for introducing new technologies. The use of advanced engineering ceramics and composites in the aerospace and defense industries is continuous and increases day by day. Surface Roughness (SR) prediction plays a vital role in improving the surface finish in industries. The present work deals with predicting the surface roughness of AISI1020 steel material in Electrical Discharge Machining (EDM) by using Adaptive Neuro Fuzzy Inference System (ANFIS). Methods/Statistical Analysis: The discharge voltage, discharge current, pulse on time, pulse off time, gap between tool and workpiece and oil pressure are taken as the input parameters, whereas SR is the output machining parameter. Design of Experiment (DoE) is based on Response Surface Methodology (RSM). ANFIS model has been constructed using Gaussian membership function (Gaussmf) with 2 membership function for each and every input parameter and linear membership function for output parameter SR and MRR. Findings: We employed both the back propagation method and the hybrid method for membership function parameter training. Based on the conclusion from the comparison of ANFIS with two types of membership function parameter training, hybrid method provides accurate results. Applications/Improvements: It is further used that the maximum error when the network is optimized by the intelligent technique has been reduced considerably. Sensitivity analysis is also done to find the relative influence of factors on the performance measures. It is observed that type of material is having more influence on the performance measures.

Comparison of Fuzzy Logic and Neural Network for Modelling Surface Roughness in EDM

Surface roughness is the main indicator of technological performances of a component for electrical discharge machining (EDM). EDM process of manganese alloyed cold-work tool steel was modelled. In this paper we used the fuzzy logic (FL) and neural network (NN) to predict the effect of machining variables (discharge current and pulse duration) on the surface roughness of manganese alloyed cold-work tool steel in order to improve and increase its range of application. The experiments are carried out on manganese alloyed cold-work tool steel, processed with electrodes made of copper. The values of surface roughness predicted by these models are then compared. All models show good agreement with experimental results. When compared to the NN and FL models, the NN model has shown a significant forecast improvement. The results indicate that the NN model is an effective algorithm to forecast the surface roughness in EDM.

Modeling of machining parameters of Ti-6Al-4V for electric discharge machining: A neural network approach

This paper presents the artificial intelligence model to predict the optimal machining parameters for Ti-6Al-4V through electrical discharge machining (EDM) using copper as an electrode and positive polarity of the electrode. The objective of this paper is to investigate the peak current, servo voltage, pulse on- and pulse off-time in EDM effects on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Radial basis function neural network (RBFNN) is used to develop the artificial neural network (ANN) modeling of MRR, TWR and SR. Design of experiments (DOE) method by using response surface methodology (RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out through analysis of variance (ANOVA). The optimum machining conditions are estimated and verified with proposed ANN model. It is observed that the developed model is within the limits of the agreeable error with experimental results. Sensitivity analysis is carried out to investigate the relative influence of factors on the performance measures. It is observed that peak current effectively influences the performance measures. The reported results indicate that the proposed ANN models can satisfactorily evaluate the MRR, TWR as well as SR in EDM. Therefore, the proposed model can be considered as valuable tools for the process planning for EDM and leads to economical industrial machining by optimizing the input parameters. Key words: Ti-6AL-4V, material removal rate, tool wear rate, surface roughness, radial basis function neural network, response surface method.

Modeling and Analysis of Process Parameters on Surface Roughness in EDM of AISI D2 Tool Steel by RSM Approach

In this research, Response Surface Methodology (RSM) is used to investigate the effect of four controllable input variables namely: discharge current, pulse duration, pulse off time and applied voltage Surface Roughness (SR) of on Electrical Discharge Machined surface. To study the proposed second-order polynomial model for SR, a Central Composite Design (CCD) is used to estimation the model coefficients of the four input factors, which are alleged to influence the SR in Electrical Discharge Machining (EDM) process. Experiments were conducted on AISI D2 tool steel with copper electrode. The response is modeled using RSM on experimental data. The significant coefficients are obtained by performing Analysis of Variance (ANOVA) at 5% level of significance. It is found that discharge current, pulse duration, and pulse off time and few of their interactions have significant effect on the SR. The model sufficiency is very satisfactory as the Coefficient of Determination (R 2 ) is found to be 91.7% and adjusted R 2 -statistic (R 2j) 89.6%.

Artificial neural network models for the prediction of surface roughness in electrical discharge machining

In the present paper Artificial Neural Networks (ANNs) models are proposed for the prediction of surface roughness in Electrical Discharge Machining (EDM). For this purpose two well-known programs, namely Matlab® with associated toolboxes, as well as Netlab®, were emplo- yed. Training of the models was performed with data from an extensive series of EDM experiments on steel grades; the proposed models use the pulse current, the pulse duration, and the processed material as input parameters. The reported results indicate that the proposed ANNs models can satisfactorily predict the surface roughness in EDM. Moreover, they can be considered as valuable tools for the process planning for EDMachining.