Research of EDM(Electrical Discharge Machining) Process Simulation Based on Grey Neural Network

Lin, Zhongyuan; Liu, Yong; Zhang, Litang

doi:10.1007/978-3-642-31516-9_60

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Abulais [8] has researched on various types of EDM like ultrasonic vibration dry EDM, powder-based EDM, and also used water as the dielectric fluid. Lin et al [9] used Grey Neural Network on EDM and verified that the data are very similar to the actual experimental result. Ni [10] has discussed the various type of application of the Artificial Neural Network (ANN) in various uneasy condition and achieved a key technology for this application.…”

Section: Introductionmentioning

confidence: 59%

Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process

Bose¹,

Pain²

2021

Computational Optimization Techniques and Applications

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As the modern day, the technologies are approaching with high accuracy at the same time with low material costing, non-traditional machining is very much essential to sustain in this modern manufacturing system. In this present research work Electric Discharge Machining (EDM) is used with different types of tools like Copper, Aluminium, and Brass are used while machining High Carbon High Chromium (HCHCr), Hot Die Steel (HDS) and Oil Hardened Nitride Steel (OHNS) workpiece material. This research work is aimed to find out the most efficient tool material for different workpiece materials while satisfying the contradictory objectives of high material removal rate (MRR) and low Tool Wear Rate (TWR). The experimental data are trained and validated by using Artificial Neural Network (ANN). Finally, the results obtained through Genetic Algorithm are hybridized with a Fuzzy- Multi Criteria Decision Making (MCDM) technique to obtain a single parametric combination of the process control parameters which satisfies these two contradictory objectives function simultaneously.

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Section: Introductionmentioning

confidence: 59%

Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process

Bose¹,

Pain²

2021

Computational Optimization Techniques and Applications

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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.…”

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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Multi Parametric Investigation for Improvement in Machining Characteristics on Aluminium Boron Carbide Composite in WEDM

Sahu,

Singh,

Sahu

et al. 2023

Springer Proceedings in Materials

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Research of EDM(Electrical Discharge Machining) Process Simulation Based on Grey Neural Network

Cited by 4 publications

References 2 publications

Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process

Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process

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

Multi Parametric Investigation for Improvement in Machining Characteristics on Aluminium Boron Carbide Composite in WEDM

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