scholarly journals Prediction of surface roughness and material removal rate in wire electrical discharge machining on aluminum based alloys/composites using Taguchi coupled Grey Relational Analysis and Artificial Neural Networks

2019 ◽  
Vol 472 ◽  
pp. 22-35 ◽  
Author(s):  
Titus Thankachan ◽  
K. Soorya Prakash ◽  
R Malini ◽  
S. Ramu ◽  
Prabhu Sundararaj ◽  
...  
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Wire Electrical Discharge Machining ◽  
Relational Analysis ◽  
Grey Relational

Soft Modeling of Wire Electrical Discharge Machining of WC-Co Composite

2011 ◽  
Vol 335-336 ◽  
pp. 535-540 ◽  
Author(s):  
Veluswamy Muthuraman ◽  
Raju Ramakrishnan
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Wire Electrical Discharge Machining ◽  
Ann Model

The prediction of optimal machining conditions for required surface roughness and material removal rate (MRR) plays a very significant role in process planning of wire electrical discharge machining (WEDM). Artificial neural networks (ANN) are widely applied to predict the performance characteristics of complex machining process like WEDM very accurately. This present work deals with the features of cutting operation by WEDM of tungsten carbide- cobalt composite(WC – Co) and an artificial neural networks(ANN) model in terms of machining parameters, developed to predict surface roughness(Ra) and material removal rate (MRR).The experiment was planned as per Taguchi’s L 27 orthogonal array. The predictive capacity of the models was validated. The test results indicate that the proposed models could adequately describe the performance indicators with the limits of the factors that are being investigated. Finally the accuracy of the developed ANN model was compared to the experimental values. It was observed that the proposed ANN model is good.

scholarly journals Multi objective optimization for kerf and material removal rate in wire electrical discharge machining using Taguchi method combined grey relational analysis

2018 ◽  
Vol 204 ◽  
pp. 06006 ◽  
Author(s):  
Pathya Rupajati ◽  
M. Kurniadi Rasyid ◽  
Ali Nurdin
Keyword(s):  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Grey Relational ◽  
Wire Feed

This paper investigated the multi performance characteristics of wire electrical discharge machining for an optimal machining parameters to get low kerf and high material removal rate at the same time. The machining parameters i.e arc on time, on time, servo voltage and wire feed were used in this experiment. Based on L9 orthogonal array, the signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) was used to study the machining parameters of DIN 1.2510 tool steel. Multi response characteristics were solved by Taguchi method combined grey relational analysis. Experimental results are provided to demonstrate the effectiveness of this method i.e. kerf decreased from 354 μm to 345 μm, while material removal rate (MRR) increased from 9,313 mm3/min to 13,989 mm3/min. From the optimization result validated in the confirmation experiment the machining parameters combination that could produce the optimum responses are arc on time of 2 A, pulse on time of 8 μs, servo voltage 80 V and wire feed 60 mm/min.

scholarly journals Analysis and Optimization of the Machining Characteristics of High-Volume Content SiCp/Al Composite in Wire Electrical Discharge Machining

Crystals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1342
Author(s):  
Hongzhi Yan ◽  
Bakadiasa Djo Kabongo ◽  
Hongbing Zhou ◽  
Cheng Wu ◽  
Zhi Chen
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
High Volume ◽  
Wire Electrical Discharge Machining ◽  
Al Composite

With the properties of high specific strength, small thermal expansion and good abrasive resistance, the particle-reinforced aluminum matrix composite is widely used in the fields of aerospace, automobile and electronic communications, etc. However, the cutting performance of the particle-reinforced aluminum matrix composite is very poor due to severe tool wear and low machining efficiency. Wire electrical discharge machining has been proven to be a good machining method for conductive material with any hardness. Even so, the high-volume SiCp/Al content composite is still a difficult-to-machine material in wire electrical discharge machining due to the influence of insulative the SiC particle. The goal of this paper is to analyze the machining characteristics and find the optimal process parameters for the high-volume content (65 vol.%) SiCp/Al composite in wire electrical discharge machining. Experimental results show that the material removal method of the SiCp/Al composite includes sublimating, decomposing and particle shedding. The material removal rate is found to increase with the increasing pulse-on time, first increasing and then decreasing with the increasing pulse-off time, servo voltage, wire feed and wire tension. Pulse-on time and servo voltage are the dominant factors for surface roughness. In addition, the multi-objective optimization method of the nondominated neighbor immune algorithm is presented to optimize the process parameters for a fast material removal rate and low surface roughness. The optimized process parameters can increase the material removal rate by 34% and reduce the surface roughness by 6%. Furthermore, the effectiveness of the Pareto optimal solution is proven by the verified experiment.

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