Modeling of EDM responses

The results indicated that in order to obtain a high value of MRR within the work interval of this study, Ton should be fixed as low as possible, and conversely, the larger the selected I and U. And the optimal value of MRR was 139.126 mg/min at optimal process parameters I = 10 A, U = 90 V and Ton = 100 s. The mathematical model for the MRR can be effectively employed for the optimal process parameters selection in diesinking EDM for SKD11 die steel. Empirical tests show that the model can calculate quite accurately predicted by MRR (error  0.6%).

8p quilen 30-11-2019 14 0   Download

This paper aims to develop a combination of Taguchi and fuzzy TOPSIS methods to solve multi-response parameter optimization problems in green manufacturing. Electrical Discharge Machining (EDM), a commonly used non-traditional manufacturing process was considered in this study.

6p cdktkttn 02-06-2018 51 1   Download

(BQ) In this paper, attempts have been made to model and optimize process parameters in Electro-Discharge Machining (EDM) of tungsten carbide-cobalt composite (Iso grade: K10) using cylindrical copper tool electrodes in planing machining mode based on statistical techniques.

6p xuanphuongdhts 27-03-2017 31 1   Download

(BQ) In this paper, working current, working voltage, oil pressure, spark gap Pulse On Time and Pulse Off Time on Material Removal Rate (MRR) and Surface Finish (Ra) has been studied. Empirical models for MRR and Ra have been developed by conducting a designed experiment based on the Grey Relational Analysis. Genetic Algorithm (GA) based multi-objective optimization for maximization of MRR and minimization of Ra has been done by using the developed empirical models. Optimization results have been used for identifying the machining conditions.

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(BQ) Electrical discharge machining (EDM) is inherently a stochastic process. Predicting the output of such a process with reasonable accuracy is rather difficult.

19p xuanphuongdhts 27-03-2017 39 1   Download

(BQ) The newly engineered metal matrix composite (MMC) of aluminium 7075 reinforced with 10 wt% of B4C particles were prepared by stir casting method. Experiments were carried out by adopting face centered central composite design of response surface methodology.

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(BQ) Present study attempts to model and optimize the complex electrical discharge machining (EDM) process using soft computing techniques. Artificial neural network (ANN) with back propagation algorithm is used to model the process. As the output parameters are conflicting in nature so there is no single combination of cutting parameters, which provides the best machining performance. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process.

9p xuanphuongdhts 27-03-2017 56 2   Download