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, Nishant; Singh, Yashvir; Kumar, Sanjeev; Sharma, Abhishek

doi:10.1007/s42452-019-0545-x

Cited by 17 publications

(5 citation statements)

References 24 publications

(28 reference statements)

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“…Finding reveals that developed model predict the process output with minimum mean error. Singh et al [25] developed statistical and soft computing-based models and performed the comparative studies during EDM of die steel. Finding suggested that actual and values predicted by developed models are in good agreement.…”

Section: Introductionmentioning

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.

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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.

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Section: Introductionmentioning

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.

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“…3.1 Electrode selection for machining of AZ91/5B4C composites EDM was specifically used for the machining of hard materials (Ahmed et al, 2019). An enormous amount of research work has been carried out with the goal of finding an optimal process parameter by varying the input variables of EDM (Singh et al, 2019;Nair et al, 2019). The machining characteristic of EDM depends greatly on the selection of the right combination of the electrode and the tool material.…”

Section: Decision-making Problemsmentioning

confidence: 99%

Integrated MOORA-ELECTRE approach for solving multi-criteria decision problem

Ranjith¹,

Vimalkumar²

2021

WJE

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Purpose The most difficult tasks in the design and development of products for diverse engineering applications were the selection of suitable materials. Choice of inappropriate process variables leads to poor performance, which increases the cost of the product. The selection of the best option of available alternatives is important to improve the performance and productivity of the manufacturing enterprises. Design/methodology/approach The paper aims to develop Hybrid Multi-Criteria Decision Making (HMCDM) by integrating two potential optimization techniques Elimination Et Choix Traduisant la REalité and multi-objective optimization on the basis of ratio analysis. The weight of the criteria was calculated using the critic weight method. Findings The efficiency and flexibility of the proposed HMCDM technique were illustrated and validated by two examples. In the first case, the best electrode material among the five available alternatives was selected for the electrical discharge machining of AZ91/B4Cp magnesium composites. In the second case, the optimum weight percentage of composites providing the best tribological properties was chosen. Originality/value It was noted that the HMCDM methodology was quite simple to comprehend, easy to apply and provided reliable rankings of the material alternatives. The proposed hybrid algorithm is suitable for product optimization as well as design optimization.

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“…Two groups of experiments were performed, just with the kerosene dielectric and then by adding the SiC micro powders mixing to dielectric uid. Singh [14] studied the statistical and soft computer-based predictive models for the surface roughness (SR) and material removal rate (MRR) during the EDM process of die steel which was helium-assisted. They tried to develop mathematical models to predict the MRR and SR.…”

Section: Introductionmentioning

confidence: 99%

New EDM Generator Used for the Machining of 1.2363 and 1.2343ESR Steels

Mouralová

Bednár

Beneš

et al. 2021

Preprint

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The basic building block of any electric discharge machine is a generator, which ensures the operation of material separation itself. Not only the erosion rate and electricity consumption but also the quality of the machined surfaces depend on the design of the generator. These key factors for efficient machining have been investigated using a new energy-saving and powerful generator developed for the electric discharge machine (EDM) while machining 1.2363 and 1.2343ESR steels. In order to monitor and model the responses in the form of eroding rate and surface quality, a two-level half-factor experiment was performed with one replication at the corner points and two replications at the central points, for a total of 80 rounds. Both graphite and copper electrodes of sizes 10x10 mm and 100x100 mm were used for eroding and the influence of parameter settings: Open-voltage, Pulse current, Pulse on-time and Pulse off-time was monitored. It was found out that the shape of the electrode and Pulse current have the most significant effect on the erosion rate. The parameters Pulse current, Pulse on-time and their mutual interaction have the most significant effect on the surface topography. Statistically significant factors influencing the occurrence of defects turned out to be Pulse current, Pulse on-time and Material of workpiece, where it can be seen that the material 1.2343ESR is significantly less prone to the formation of surface defects.

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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

Cited by 17 publications

References 24 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

Integrated MOORA-ELECTRE approach for solving multi-criteria decision problem

New EDM Generator Used for the Machining of 1.2363 and 1.2343ESR Steels

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