EDM with an Air-Assisted Multi-Hole Rotating Tool

This research work discusses the application of three intelligent prediction models, based on artificial neural network (ANN) with back-propagation algorithm, adaptive neuro-fuzzy inference system (ANFIS) and hybrid ANFIS and genetic algorithm (ANFIS-GA). These techniques are used for prediction and comparison of machining aspects such as material removal rate (MRR) and surface roughness during gas-assisted electrical discharge machining of D3 die steel. In the present work, helium-assisted EDM with perforated tool has been performed. In this work, parameters considered for machining are discharge current, pulse on time, duty cycle, tool rotation and discharge gas pressure. The suggested approach is based on up-gradation of ANFIS with GA. The GA algorithm is applied to improve the precision of the ANFIS model. The soft computing models were trained, tested and validated with experimental data. Mean square error (MSE), mean absolute error (MAE), root-mean-square error and correlation coefficient (R 2), were used to measure the efficacy of models predicting abilities developed through ANN, ANFIS and hybrid ANFIS-GA approaches. The experiment and anticipated measure of MRR and SR of the process, acquired by ANN, ANFIS and hybrid ANFIS-GA, was found to be in good agreement. The prediction potential of proposed models was tested using new set of data for the training and testing process. The ANFIS-GA technique provides more accurate prediction of the responses in comparison with the ANN and the ANFIS. In general, the inference of this work discloses that the hybrid algorithm like ANFIS-GA is an efficient and effective approach for precise prediction of EDM process responses.

show abstract

“…Material removal rate was found as the ratio of the weight of eroded specimen to machining time [26]: (1) Material removal rate(mg∕min) =…”

Section: Details Of Tool Designmentioning

confidence: 99%

Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses

Singh

Kumar

et al. 2019

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…They also undertook a comparative analysis of process responses by using bundled tool and by using conventional solid tool while conducting EDM operation. Singh et al [10] studied the effect of air-assisted multi-hole rotary tool electrodes during the EDM process. They found that its use improved the MRR and reduced the EWR as compared to solid rotary tool electrodes under the same machining conditions.…”

Section: Literature Review With Aspects Of Improved Machining Performmentioning

confidence: 99%

“…The present study examined effects of various process factors on output through the CCRD method. A total of 32 experiments were conducted using the CCRD method [10] with independent variables at 5 different levels. The machined specimens were cleaned with acetone.…”

Section: Details Of Tool Designmentioning

confidence: 99%

Comparative study of statistical and soft computing-based predictive models for material removal rate and surface roughness during helium-assisted EDM of D3 die steel

Singh

Kumar

et al. 2019

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

This research work proposes mathematical models, based on artificial neural network (ANN) with back-propagation algorithm, adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM), for prediction of material removal rate (MRR) and surface roughness (SR) of helium-assisted electrical discharge machining of D3 die steel. The helium gas-assisted die-sinking EDM with perforated electrode was carried out by an EDM machine. For the present experimental work, discharge current, pulse on time, duty cycle, electrode rotation and discharge gas pressure were selected as process factors, while MRR and SR were chosen as process responses. Analysis of variance (ANOVA) was done to examine the adequacy of the developed model. The fit summary confirmed that the quadratic model is statistically appropriate and the lack of fit is insignificant. Root mean square error and absolute standard deviation, obtained through RSM, were also used for developing the model and for its predicting abilities through ANN and ANFIS. The experimental and predicted values of MRR and SR during the process, obtained by RSM, ANN and ANFIS, were found to be in accord with each other. However, the ANFIS technique proved to be more fitting to the responses as compared to the ANN and the RSM. The optimum value of the MRR at 28.54 mg/min and the SR at 4.21 µm was obtained with optimal process parameters by optimization of developed statistical models using genetic algorithm.

show abstract

“…Some researchers have been reported the use of different shape Cu electrodes and multihole electrodes to predict the machinability in the EDM process [28][29][30][31][32][33][34][35][36][37][38][39]. Wl-Taweel [28] concluded that the Al-Cu-Si-TiC powder sintered electrodes are more sensitive to T on (pulse on time) and peak current.…”

Section: Introductionmentioning

confidence: 99%

Influence on machinability and form tolerance of Inconel 718 in Edm using different diameter multi hole Cu electrodes

Kumar

Dhanabalan²

2019

SN Appl. Sci.

View full text Add to dashboard Cite

This research aims to examine the machinability and form tolerances in die sinking EDM for Ni-based superalloy with different diameter internally engraved hole of the multi-hole copper electrode. Manufacturing of superalloys with close tolerances is becoming a major area for primary research in defence and aerospace industries due to their particular characteristics. Therefore, this work focuses on an experimental analysis to improve the productivity and Material Removal Rate of Inconel-718 with close tolerances. The most dominating process constraints, viz. hole diameter of Multi-hole electrode (D), Peak current (I p ), Pulse on Time (T on ) and dielectric fluid pressure were selected as process constraints to conduct experimental trials. The Material Removal Rate, EWR and form tolerances were considered as output responses. The experimental outcomes were optimized by Taguchi analysis and multi-parametric optimization method GRA. A Fuzzy logic model using Matlab was developed for the prediction of performance parameters, namely MRR, EWR, circularity and cylindricity with respect to changes in input parameters. Three MF's ('Trimf-triangular membership functions') were allocated to each input response, whereas five MF's were allocated to output responses. The Fuzzy model shows the 94.01% accuracy between the experimental values and the predicted values. From the results, it is concluded that the use of a multi-hole electrode leads to a higher rate of MRR. Moreover, the form tolerance deviation improves with the increment of T on value. The optimized parameters showed a considerable improvement in the process and will facilitate the aerospace and defence industries to improve the productivity of Inconel-718 with close tolerance.

show abstract

EDM with an Air-Assisted Multi-Hole Rotating Tool

Cited by 54 publications

References 22 publications

Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses

Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses

Comparative study of statistical and soft computing-based predictive models for material removal rate and surface roughness during helium-assisted EDM of D3 die steel

Influence on machinability and form tolerance of Inconel 718 in Edm using different diameter multi hole Cu electrodes

Contact Info

Product

Resources

About