Multi-objective process optimization of wire electrical discharge machining based on response surface methodology

2013 ◽  
Vol 36 (2) ◽  
pp. 301-313 ◽  
Author(s):  
Danial Ghodsiyeh ◽  
Abolfazl Golshan ◽  
S. Izman
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Optimization ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Wire Electrical Discharge Machining ◽  
Multi Objective

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.

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.

scholarly journals Optimization of the Surface Roughness Parameters of Ti–Al Intermetallic Based Composite Machined by Wire Electrical Discharge Machining

Coatings ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 900 ◽  
Author(s):  
Sonia Ezeddini ◽  
Mohamed Boujelbene ◽  
Emin Bayraktar ◽  
Sahbi Ben Salem
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Taguchi Method ◽  
Response Surface ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Roughness Parameters ◽  
Surface Roughness Parameters

This work presents a comprehensive research using the Taguchi method and response surface methodology (RSM) to predict surface roughness parameters in wire electrical discharge machining (WEDM) manufacturing for a novel Ti–Al intermetallic based composite that was developed at Supmeca, a composite design laboratory for aeronautical applications in Paris, France. At the first stage, a detailed microstructure analysis was carried out on this composite. After that, the cutting parameters of the WEDM process were determined: Start-up voltage U, Pulse-on-time Ton, speed advance S and flushing pressure p were selected to find out their effects on surface roughness Ra. In the second stage, analyses of variance (ANOVA) were used as the statistical method to define the significance of the machining parameters. After that, an integrated method combining the Taguchi method and the response surface methodology (RSM) was used to develop a predictive model of the finish surface. The microstructure of the surface and subsurface of the cut edge, the micro-cracks, debris and craters and surface roughness of the specimens cut at the altered conditions were evaluated by scanning electron microscopy (SEM) and 3D-Surfscan.

Sign in / Sign up

Export Citation Format

Share Document