scholarly journals AHP-DENG’S Similarity Based Optimization of WEDM Process Parameters of Al/SiCp Composite

2017 ◽  
Author(s):  
K. Anand Babu ◽  
P. Venkataramaiah ◽  
P. Dileep
Keyword(s):  
Process Parameters ◽  
Wedm Process

scholarly journals Optimization of WEDM Process Parameters on Machining of AZ91 Magnesium alloy using MOORA method

2018 ◽  
Vol 390 ◽  
pp. 012107 ◽  
Author(s):  
A. Muniappan ◽  
Mantri Sriram ◽  
C. Thiagarajan ◽  
G. Bharathi Raja ◽  
T. Shaafi
Keyword(s):  
Magnesium Alloy ◽  
Process Parameters ◽  
Az91 Magnesium Alloy ◽  
Az91 Magnesium ◽  
Moora Method ◽  
Wedm Process

scholarly journals Multi Quality Characteristics Of WEDM Process Parameters with RSM

2013 ◽  
Vol 64 ◽  
pp. 710-719 ◽  
Author(s):  
Neeraj Sharma ◽  
Rajesh Khanna ◽  
Rahuldev Gupta
Keyword(s):  
Process Parameters ◽  
Quality Characteristics ◽  
Wedm Process

Machining Characteristics Estimation in WEDM Process While Machining Titanium Grade-2 Material Using ANN

2020 ◽  
Author(s):  
Prathik Jain Sudhir ◽  
Ravindra Holalu Venkatadas ◽  
Ugrasen Gonchikar
Keyword(s):  
Process Parameters ◽  
Machining Process ◽  
Electrode Wear ◽  
Training Set ◽  
Hard Materials ◽  
Titanium Grade ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process ◽  
Machining Characteristics

Abstract Wire Electrical Discharge Machining (WEDM) provides an effective solution for machining hard materials with intricate shapes. WEDM is a specialized thermal machining process is capable to accurately machining parts of hard materials with complex shapes. However, selection of process parameters for obtaining higher machining efficiency or accuracy in wire EDM is still not fully solved, even with the most up-to-date CNC WED machine. The study presents the machining of Titanium grade 2 material using L’16 Orthogonal Array (OA). The process parameters considered for the present work are pulse on time, pulse off time, current, bed speed, voltage and flush rate. Among these process parameters voltage and flush rate were kept constant and the other four parameters were varied for the machining. Molybdenum wire of 0.18mm is used as the electrode material. Titanium is used in engine applications such as rotors, compressor blades, hydraulic system components and nacelles. Its application can also be found in critical jet engine rotating and airframes components in aircraft industries. Firstly optimization of the process parameters was done to know the effect of most influencing parameters on machining characteristics viz., Surface Roughness (SR) and Electrode Wear (EW). Then the simpler functional relationship plots were established between the parameters to know the possible information about the SR and EW. This simpler method of analysis does not provide the information on the status of the material and electrode. Hence more sophisticated method of analysis was used viz., Artificial Neural Network (ANN) for the estimation of the experimental values. SR and EW parameters prediction was carried out successfully for 50%, 60% and 70% of the training set for titanium material using ANN. Among the selected percentage data, at 70% training set showed remarkable similarities with the measured value then at 50% and 60%.

Multi-Response Optimization of Micro-WEDM Process Parameters of Ti49.4-Ni50.6 Shape Memory Alloy for Orthopedic Implant Application

2018 ◽  
Vol 1150 ◽  
pp. 1-21
Author(s):  
Adik M. Takale ◽  
Nagesh K. Chougule ◽  
Preetam H. Selmokar ◽  
M.G. Gawari
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Process Parameters ◽  
Removal Rate ◽  
Orthopedic Implant ◽  
Wire Tension ◽  
Response Optimization ◽  
Micro Wedm ◽  
Wedm Process ◽  
Wire Feed

The present work deals with the optimization of micro-WEDM process parameters for machining Ti49.4-Ni50.6 shape memory alloy (SMA) for orthopedic implant application. Effect of micro-WEDM parameters viz. Gap voltage, capacitance, wire feed and wire tension on the response variables such as material removal rate, surface roughness, kerf width and dimensional deviation is determined. As Ti-Ni SMA has fascinating properties and bio-compatibility, have been considered for present work. Nine experiments have been performed on micro-WEDM based on an orthogonal array of Taguchi method. Subsequently, the grey relational analysis (GRA) method is applied to determine an optimal set of process parameters. It is observed that optimized set of parameters A3B3C3D1 viz. 140 V gap voltage, 0.4 µF capacitance, wire feed 30 µm/sec and 30% of wire tension determined by using GRA offers maximum MRR and minimum SR, KW and DD. From the Analysis of Variance, it is seen that the process parameter capacitance is the most significant parameter for multi-response optimization with a percentage contribution of 77.91%. Young’s modulus also checked for biocompatibility. Also, SEM images are taken to confirm the results offering better surface quality. Heat treatment process like annealing is found to be the most suitable to recover shape memory effect of WEDMed samples.

Optimization of WEDM process parameters for machining Al 2219 alloy

2020 ◽  
Vol 26 ◽  
pp. 2541-2545
Author(s):  
Ankit Singh Rana ◽  
Amit Joshi ◽  
Sunil Chamoli ◽  
Chandraveer Singh Kanawat ◽  
Pawan Kumar Pant
Keyword(s):  
Process Parameters ◽  
Wedm Process

scholarly journals Empirical Modeling of Average Cutting Speed during WEDM of Hastelloy C22

2018 ◽  
Vol 249 ◽  
pp. 02003 ◽  
Author(s):  
Bhupinder Singh ◽  
Joy Prakash Misra
Keyword(s):  
Process Parameters ◽  
Cutting Speed ◽  
Performance Measure ◽  
Empirical Modeling ◽  
Workpiece Material ◽  
Peak Current ◽  
The Right ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process

Cutting speed (CS) is a key performance measure to achieve optimal utilization of the WEDM process. However, input process parameters of WEDM and combination of wire and workpiece material greatly hamper CS and hence productivity and machining efficiency. Therefore, it is essential to pick the right combination of parameters and wire and workpiece material to obtain better CS. In this paper, four process parameters: Pulse-on time, Pulse-off time, Spark-gap voltage, and Peak current were chosen to develop an empirical model for CS during WEDM of Hastelloy C22 to provide a guideline to the potential users of the technique. This paper describes the response surface methodology (RSM) based mathematical modeling for average cutting speed. Furthermore, analysis of variance (ANOVA) was applied to find out significant process parameters and it was depicted that pulse on time and peak current were the major parameters affecting CS.

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