Predicting the material removal rate during electrical discharge diamond grinding using the Gaussian process regression: a comparison with the artificial neural network and response surface methodology

2021 ◽  
Vol 113 (5-6) ◽  
pp. 1527-1533
Author(s):  
Yun Zhang ◽  
Xiaojie Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Gaussian Process ◽  
Response Surface ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Removal Rate ◽  
Gaussian Process Regression

scholarly journals Integrated optimization model in wire electric discharge machining using Gaussian process regression and wolf pack algorithm approach while machining SiCp/Al composite

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401878740 ◽  
Author(s):  
Jun Ma ◽  
Wuyi Ming ◽  
Jinguang Du ◽  
Hao Huang ◽  
Wenbin He ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Material Removal Rate ◽  
Optimization Model ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Gaussian Process Regression ◽  
Wire Electrical Discharge Machining ◽  
Al Composite

To further improve prediction accuracy and optimization quality of wire electrical discharge machining of SiCp/Al composite, trim cuts were performed using Taguchi experiment method to investigate the influence of cutting parameters, such as pulse duration ( Ton), pulse interval ( Toff), water pressure ( Wp), and wire tension ( Wt)), on material removal rate and three-dimensional surface characteristics ( Sq and Sa). An optimization model to predict material removal rate and surface quality was developed using a novel hybrid Gaussian process regression and wolf pack algorithm approach based on experiment results. Compared with linear regression model and back propagation neural network, the availability of Gaussian process regression is confirmed by experimental data. Results show that the worst average predictive error of five independent tests for material removal rate, Sq, and Sa are not more than 10.66%, 19.85%, and 22.4%, respectively. The proposed method in this article is an effective method to optimize the process parameters for guiding the actual wire electrical discharge machining process.

scholarly journals Optimization of EDM Process Parameter for MRR and SR of 17- 4 PH Steel Using Response Surface Methodology

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
SHUBHRATA NAGPAL
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Optimization Techniques ◽  
Pulse On Time

In the present work, an attempt has been made for material removal rate and surface roughness by response surface optimization techniques in Electrical discharge machining. Electrical discharge machining, commonly known as EDM, is a process that is used to remove metal through the action of an electrical discharge of short duration and high current density between the work piece and too. This work presents the results of a mathematical investigation carried out to the effects of machining parameters such as current, pulse on time, pulse off time and lift time on material removal rate and surface roughness in electrical discharge machining of 17-4 PH steel by using copper electrode. Response surface methodology and ANOVA techniques are used for data analysis to solve the multi-response optimization. To validate the optimum levels of the parameter, confirmation run was performed by setting the parameters at optimum levels. Material Removal Rate during the process has been taken as productivity estimate with the objective to maximize it. With an intention of minimizing surface roughness is been considered as most important output parameter. It is found that the good agreement of that current is most significant parameter for material removal rate and less for surface roughness followed by pulse on time and lift 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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