Effect of process parameters in electric discharge machining of D2 steel and estimation of coefficient for predicting surface roughness

2018 ◽  
Vol 20 (2) ◽  
pp. 101 ◽  
Author(s):  
Deepak Doreswamy ◽  
Jatin Javeri
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Electric Discharge ◽  
Electric Discharge Machining ◽  
D2 Steel

scholarly journals Synthesis and Characterization of Mechanical Properties and Wire Cut EDM Process Parameters Analysis in AZ61 Magnesium Alloy + B4C + SiC

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3689
Author(s):  
Thanikodi Sathish ◽  
Vinayagam Mohanavel ◽  
Khalid Ansari ◽  
Rathinasamy Saravanan ◽  
Alagar Karthick ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Electric Discharge ◽  
Material Removal ◽  
Removal Rate ◽  
Stir Casting ◽  
Electric Discharge Machining ◽  
Az61 Magnesium Alloy

Wire Cut Electric Discharge Machining (WCEDM) is a novel method for machining different materials with application of electrical energy by the movement of wire electrode. For this work, an AZ61 magnesium alloy with reinforcement of boron carbide and silicon carbide in different percentage levels was used and a plate was formed through stir casting technique. The process parameters of the stir casting process are namely reinforcement %, stirring speed, time of stirring, and process temperature. The specimens were removed from the casted AZ61 magnesium alloy composites through the Wire Cut Electric Discharge Machining (WCEDM) process, the material removal rate and surface roughness vales were carried out creatively. L 16 orthogonal array (OA) was used for this work to find the material removal rate (MRR) and surface roughness. The process parameters of WCEDM are pulse on time (105, 110, 115 and 120 µs), pulse off time (40, 50, 60 and 70 µs), wire feed rate (2, 4, 6 and 8 m/min), and current (3, 6, 9 and 12 Amps). Further, this study aimed to estimate the maximum ultimate tensile strength and micro hardness of the reinforced composites using the Taguchi route.

INVESTIGATION OF EFFECTS OF DIELECTRIC TYPE AND POLARITY ON ELECTRIC DISCHARGE MACHINING OF AISI D2 STEEL USING RESPONSE SURFACE METHODOLOGY

2019 ◽  
Vol XVI (4) ◽  
pp. 81-93
Author(s):  
Muhammad Hanif ◽  
Wasim Ahmad ◽  
Salman Hussain ◽  
Mirza Jahanzaib
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electric Discharge ◽  
Discharge Current ◽  
Removal Rate ◽  
Electric Discharge Machining ◽  
Aisi D2 Steel ◽  
Aisi D2 ◽  
D2 Steel

This paper presents a study on optimisation of process parameters of die-sinking electric discharge machining. The influence of dielectric type, electrode polarity, discharge current and gap on the material removal rate (MRR) and surface roughness for machining of AISI D2 steel have been studied. Response surface methodology suggested in the existing literature was employed for conducting the experiments. Kerosene and transformer oils were found as the best dielectric for MRR and surface roughness, respectively. It was also found that electrode with positive polarity offers high MRR while negative polarity ensures best surface finish. ANOVA results indicated that discharge current was the most influencing factor affecting the performance measures, MRR and surface roughness. Although discharge gap showed low effect on MRR and surface roughness, it was effective for debris removal. Empirical models were developed to optimise the results of MRR and surface roughness.

Optimization and Prediction of Machining Responses Using Response Surface Methodology and Adaptive Neural Network by Wire Electric Discharge Machining of Alloy-X

2021 ◽  
Vol 1026 ◽  
pp. 28-38
Author(s):  
I. Vishal Manoj ◽  
S. Narendranath ◽  
Alokesh Pramanik
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Electric Discharge ◽  
Cutting Speed ◽  
Electric Discharge Machining ◽  
Surface Method ◽  
Wire Electric Discharge Machining ◽  
Pulse On Time

Wire electric discharge machining non-contact machining process based on spark erosion technique. It can machine difficult-to-cut materials with excellent precision. In this paper Alloy-X, a nickel-based superalloy was machined at different machining parameters. Input parameters like pulse on time, pulse off time, servo voltage and wire feed were employed for the machining. Response parameters like cutting speed and surface roughness were analyzed from the L25 orthogonal experiments. It was noted that the pulse on time and servo voltage were the most influential parameters. Both cutting speed and surface roughness increased on increase in pulse on time and decrease in servo voltage. Grey relation analysis was performed to get the optimal parametric setting. Response surface method and artificial neural network predictors were used in the prediction of cutting speed and surface roughness. It was found that among the two predictors artificial neural network was accurate than response surface method.

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