Adaptive Neuro-Fuzzy Interference System Modelling of EDM Process  Using CNT Infused Copper Electrode

Sethuramalingam, Prabhu; Sundararaj, Oliver

doi:10.22266/ijies2017.0630.12

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…The same phenomenon has been accounted for in WEDM [19]. Consequently, in this review, EDS range investigation is utilized to identify the components over the workpiece surface.…”

Section: Composition Of Wedm Surfacementioning

confidence: 93%

Experimental Investigation and Multi Response Optimization of WEDM Process of AA7075 Metal Matrix Composites Using Particle Swarm Optimization

Ramanan¹,

John²

2017

IJIES

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Abstract:Wire electric discharge machining (WEDM) has turned out to be one of the well-known machining procedures utilized in bringing up elaborate aspects on electrically conductive substances. To improve the performance of WEDM process an attempt was made to improve the material removal rate (MRR) and minimize surface roughness (SR) on AA7075-activated carbon composites. The machined surface integrity associated with MRR and SR have been analysed as a function of processing parameters, such as discharge current, pulse on time, pulse off time and servo speed rate. Analysis of variance (ANOVA) is carried out in the idea of identifying tremendous causes of parameters. Experimental outcome authorize the feasibility of proposed prediction strategy and the developed particle swarm optimization (PSO) algorithm. Experiment result indicates that the MRR and SR are more sensitive to a change in size than a change in volume fraction of reinforcement. AA7075-activated carbon composites have extensive applications in aerospace and marine sectors.

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“…The same phenomenon has been accounted for in WEDM [19]. Consequently, in this review, EDS range investigation is utilized to identify the components over the workpiece surface.…”

Section: Composition Of Wedm Surfacementioning

confidence: 93%

Experimental Investigation and Multi Response Optimization of WEDM Process of AA7075 Metal Matrix Composites Using Particle Swarm Optimization

Ramanan¹,

John²

2017

IJIES

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“…GA comprises three key stages namely (a) activation of the population, (b) GA operators, and (c) assessment. Fitness function is used to calculate the target condition and performance [16,29]. The issue's fitness function was determined considering the MSE between the actual and anticipated values.…”

Section: Anfis-ga and Anfis-psomentioning

confidence: 99%

Intelligent hybrid approaches for ensuring better prediction of gas-assisted EDM responses

et al. 2020

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The present research work explores the implementation of three smart hybrid predictive models based on the adaptive neuro-fuzzy inference system (ANFIS), ANFIS and genetic algorithm (GA), and ANFIS and particle swarm optimization (PSO). All such strategies have been used to determine and compare machining key elements including material removal rate (MRR) and surface roughness (SR) during the gas-assisted electrical discharge (GAEDM) process. In this study, inert gas-based EDM with a multi-hole rotating tool has been carried out. In this experimentation, pulse-on time, peak current, duty cycle, electrode rotation, and gas discharge pressure were selected as input factors. The proposed method is to upgrade ANFIS with GA and PSO techniques. The GA and PSO algorithms are used to enhance the accuracy of the ANFIS model. The models have been trained, tested, and validated with observational results. Statistical techniques were applied to assess the effectiveness of the predictive capability models established through the ANFIS, ANFIS-GA, and ANFIS-PSO techniques. The actual and predicated estimates of MRR and SR of the GAEDM, obtained by ANFIS, ANFIS-GA, and ANFIS-PSO, were observed to be as per one another. In addition, the ANFIS-PSO framework proved to be even more responsive when compared with the ANFIS and the ANFIS-GA system. In particular, the assertion of this work is that modified algorithms such as ANFIS-GA and ANFIS-PSO are an efficient and productive approach to accurate EDM response estimation.

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“…As EDM is a complex and transient micro-physical process, its stochastic material removal mechanism is affected by multiple factors, making it difficult to establish an appropriate model to investigate the relations between the input parameters and responses [4]. The past researchers have already attempted to implement different techniques, like multiple regression analysis [5][6][7][8], response surface methodology (RSM) [9][10][11][12], support vector machine (SVM) [13,14], artificial neural network (ANN) [15][16][17][18], adaptive neuro-fuzzy interference system (ANFIS) [19,20] etc. to ascertain these relationships between the input and output parameters of the EDM processes.…”

Section: Introductionmentioning

confidence: 99%

Discriminant Analysis-based Parametric Study of an Electrical Discharge Machining Process

Sarker

Chakraborty

2021

Scientia Iranica

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This paper presents the application of discriminant analysis in an electrical discharge machining (EDM) process to determine the comparative contribution of each of its input parameters on the measured responses. It also identifies the most significant EDM process parameters influencing those responses. For this process, voltage, current, pulse-on time and pulse-off time are treated as the input parameters, whereas, material removal rate, electrode wear rate and surface roughness are the responses. Based on the past and simulated experimental data, both simultaneous and step-wise estimations are carried out for each of the three responses showing the relationships between the EDM process parameters and the considered responses. It is observed that in both these estimations, pulseoff time, current and pulse-on time respectively evolve out as the most significant parameters for material removal rate, electrode wear rate and surface roughness. Step-wise estimation identifies voltage as the least significant input parameter for all these responses. The developed discriminant functions, which can also help in predicting the responses, are finally cross-validated.

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Adaptive Neuro-Fuzzy Interference System Modelling of EDM Process Using CNT Infused Copper Electrode

Cited by 7 publications

References 18 publications

Experimental Investigation and Multi Response Optimization of WEDM Process of AA7075 Metal Matrix Composites Using Particle Swarm Optimization

Experimental Investigation and Multi Response Optimization of WEDM Process of AA7075 Metal Matrix Composites Using Particle Swarm Optimization

Intelligent hybrid approaches for ensuring better prediction of gas-assisted EDM responses

Discriminant Analysis-based Parametric Study of an Electrical Discharge Machining Process

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