Modeling the machining parameters of AISI D2 tool steel material with multi wall carbon nano tube in electrical discharge machining process using response surface methodology

2012 ◽  
Vol 7 (2) ◽  
Author(s):  
S. Prabhu
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Machining Process ◽  
Machining Parameters ◽  
Steel Material ◽  
Aisi D2 ◽  
Carbon Nano Tube ◽  
Aisi D2 Tool Steel

Multi-Response Optimisation of Cutting Parameters of Wire EDM in Titanium Using Response Surface Methodology

2016 ◽  
Vol 854 ◽  
pp. 93-100 ◽  
Author(s):  
B. Sivaraman ◽  
Senthil Padmavathy ◽  
P. Jothiprakash ◽  
T. Keerthivasan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Electrical Discharge ◽  
Metal Removal ◽  
Electrical Discharge Machining ◽  
Machining Process ◽  
Work Piece ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Control Parameters

This Aim of this paper is to analyse the effect of machining parameters of wire electrical discharge machining (WEDM) on workpiece material titanium, that were now widely used in many applications because of its technical benefits. Conventional method of machining the material will make the work piece to crack or flaws due to chipping, presence of burrs and cracking. Wire cut Electrical discharge machining techniques have been already tried with some other high strength materials which is complicated to cut. To prove the feasibility of machining the titanium, many experiments were carried out based on RSM. Hence by the head wire electrical discharge machining process is to be used to machining the work piece material (titanium) and the effect of various control parameters on the response parameters were analysed and optimized and the optimal combination of control parameters were found to get higher metal removal rate and surface finish using Response Surface Methodology.

OPTIMIZATION OF ELECTRICAL DISCHARGE MACHINING PARAMETERS OF SISIC THROUGH RESPONSE SURFACE METHODOLOGY

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Abdul Azeez Abdu Aliyu ◽  
Jafri Mohd Rohani ◽  
Ahmad Majdi Abdul Rani ◽  
Hamidon Musa
Keyword(s):  
Silicon Carbide ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Industrial Applications ◽  
Machining Parameters ◽  
Difficult Time ◽  
Pulse On Time

In recent years, researchers have demonstrated increases interest in studies involving silicon carbide (SiC) materials due to several industrial applications. Extreme hardness and high brittleness properties of SiC make the machining of such material very difficult, time consuming and costly. Electrical discharge machining (EDM) has been regarded as the most viable method for the machining of SiC. The mechanism of EDM process is complex. Researchers have acknowledged a challenge in generating a model that accurately describes the correlation between the input parameters and the responses. This paper reports the study on parametric optimization of siliconized silicon carbide (SiSiC) for the following quality responses; material removal rate (MRR), tool wear ratio (TWR) and surface roughness (Ra). The experiments were planned using Face centered central composite design. The models which related MRR, TWR and Ra with the most significant factors such as discharge current (Ip), pulse-on time (Ton), and servo voltage (Sv) were developed. In order to develop, improve and optimize the models response surface methodology (RSM) was used. Non-linear models were proposed for MRR and Ra while linear model was proposed for TWR. The margin of error between predicted and experimental values of MRR, TWR and Ra are found within 6.7, 5.6 and 2.5% respectively. Thus, the excellent reproducibility of this experimental study is confirmed, and the models developed for MRR, TWR and Ra are justified to be valid by the confirmation tests.

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.

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.

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