scholarly journals Optimization of Process Parameters for Wire Electrical Discharge Machining of High Speed Steel using Response Surface Methodology

2017 ◽  
Vol V (VIII) ◽  
pp. 1993-1999
Author(s):  
Avinash K
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Electrical Discharge ◽  
High Speed ◽  
Electrical Discharge Machining ◽  
High Speed Steel ◽  
Wire Electrical Discharge Machining ◽  
Optimization Of Process Parameters

Studies of kerf width and surface roughness using the response surface methodology in AA 4032–TiC composites

2021 ◽  
pp. 095440892110414
Author(s):  
TS Senthilkumar ◽  
R Muralikannan ◽  
T Ramkumar ◽  
S Senthil Kumar
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Matrix ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Kerf Width ◽  
Wire Electrical Discharge Machining ◽  
Box Behnken Design ◽  
Pulse On Time

A substantially developed machining process, namely wire electrical discharge machining (WEDM), is used to machine complex shapes with high accuracy. This existent work investigates the optimization of the process parameters of wire electrical discharge machining, such as pulse on time ( Ton), peak current ( I), and gap voltage ( V), to analyze the output performance, such as kerf width and surface roughness, of AA 4032–TiC metal matrix composite using response surface methodology. The metal matrix composite was developed by handling the stir casting system. Response surface methodology is implemented through the Box–Behnken design to reduce experiments and design a mathematical model for the responses. The Box–Behnken design was conducted at a confident level of 99.5%, and a mathematical model was established for the responses, especially kerf width and surface roughness. Analysis of variance table was demarcated to check the cogency of the established model and determine the significant process. Surface roughness attains a maximum value at a high peak current value because high thermal energy was released, leading to poor surface finish. A validation test was directed between the predicted value and the actual value; however, the deviation is insignificant. Moreover, a confirmation test was handled for predicted and experimental values, and a minimal error was 2.3% and 2.12% for kerf width and surface roughness, respectively. Furthermore, the size of the crater, globules, microvoids, and microcracks were increased by amplifying the pulse on time.

A New Technology to Machine Bimetal Band Saw

2016 ◽  
Vol 861 ◽  
pp. 9-13
Author(s):  
Hong Jian Dong ◽  
Qin He Zhang ◽  
Lei Tan ◽  
Guo Wei Liu ◽  
Tuo Dang Guo
Keyword(s):  
Electrical Discharge ◽  
High Speed ◽  
Electrical Discharge Machining ◽  
New Technology ◽  
High Speed Steel ◽  
Processing Route ◽  
Grinding Process ◽  
Wire Electrical Discharge Machining ◽  
Low Carbon ◽  
Band Saw

As a kind of commonly used tools, junior hacksaw plays an important role in our daily life. A new kind of bimetal band saw taken low carbon medium alloy steel X32 as the backing material and the high-speed steel M42 as the saw tooth material is developed. In this paper, a new method to machine the bimetal band saw with wire electrical discharge machining (WEDM) is introduced. The processing route for common tooth profile is calculated. The fixture with specific angles is designed with CAD software (proe5.0) and machined with 3D printing technology. The experiments show that bimetal band saw machined with WEDM method has better surface quality compared with that machined through the traditional grinding process. Without any burrs, the new bimetal band saw is more resistant to wear and has a longer service life.

scholarly journals Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

2020 ◽  
Vol 4 (2) ◽  
pp. 44
Author(s):  
Vishal Lalwani ◽  
Priyaranjan Sharma ◽  
Catalin Iulian Pruncu ◽  
Deepak Rajendra Unune
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Inconel 718 ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Wire Electrical Discharge Machining ◽  
Ann Model ◽  
Control Factors

This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives.

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