scholarly journals Artificial Neural Network-Based Constitutive Relationship of Inconel 718 Superalloy Construction and Its Application in Accuracy Improvement of Numerical Simulation

2017 ◽  
Vol 7 (2) ◽  
pp. 124 ◽  
Author(s):  
Junya Lv ◽  
Huiyu Ren ◽  
Kai Gao
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Artificial Neural Network ◽  
Inconel 718 ◽  
Constitutive Relationship ◽  
Accuracy Improvement ◽  
Inconel 718 Superalloy ◽  
Artificial Neural ◽  
Relationship Of

Modeling constitutive relationship of Ti40 alloy using artificial neural network

2011 ◽  
Vol 32 (3) ◽  
pp. 1537-1541 ◽  
Author(s):  
Y. Sun ◽  
W.D. Zeng ◽  
Y.Q. Zhao ◽  
X.M. Zhang ◽  
Y. Shu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Constitutive Relationship ◽  
Artificial Neural ◽  
Relationship Of

scholarly journals Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 540-544
Author(s):  
Jayaraj JEEVAMALAR ◽  
Sundaresan RAMABALAN ◽  
Chinnamuthu SENTHILKUMAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Inconel 718 ◽  
Machining Process ◽  
Artificial Neural ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Matlab Programming ◽  
The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

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