scholarly journals An Intelligent System for Improving Electric Discharge Machining Efficiency Using Artificial Neural Network and Adaptive Control of Debris Removal Operations

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Cheng-Hsiung Lee ◽  
Ton-Shin Lai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Control ◽  
Electric Discharge ◽  
Intelligent System ◽  
Machining Efficiency ◽  
Electric Discharge Machining ◽  
Debris Removal ◽  
Artificial Neural

Artificial neural network-based adaptive control for a DFIG-based WECS

2021 ◽  
Author(s):  
S. Labdai ◽  
N. Bounar ◽  
A. Boulkroune ◽  
B. Hemici ◽  
L. Nezli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Control ◽  
Artificial Neural

Optimization and Prediction of Machining Responses Using Response Surface Methodology and Adaptive Neural Network by Wire Electric Discharge Machining of Alloy-X

2021 ◽  
Vol 1026 ◽  
pp. 28-38
Author(s):  
I. Vishal Manoj ◽  
S. Narendranath ◽  
Alokesh Pramanik
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Electric Discharge ◽  
Cutting Speed ◽  
Electric Discharge Machining ◽  
Surface Method ◽  
Wire Electric Discharge Machining ◽  
Pulse On Time

Wire electric discharge machining non-contact machining process based on spark erosion technique. It can machine difficult-to-cut materials with excellent precision. In this paper Alloy-X, a nickel-based superalloy was machined at different machining parameters. Input parameters like pulse on time, pulse off time, servo voltage and wire feed were employed for the machining. Response parameters like cutting speed and surface roughness were analyzed from the L25 orthogonal experiments. It was noted that the pulse on time and servo voltage were the most influential parameters. Both cutting speed and surface roughness increased on increase in pulse on time and decrease in servo voltage. Grey relation analysis was performed to get the optimal parametric setting. Response surface method and artificial neural network predictors were used in the prediction of cutting speed and surface roughness. It was found that among the two predictors artificial neural network was accurate than response surface method.

Other Diseases Detection

2014 ◽  
pp. 170-180
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Medical Diagnosis ◽  
Intelligent System ◽  
Associated Symptoms ◽  
Artificial Neural ◽  
Inputs And Outputs

Considering the importance of the problem of medical diagnosis, this chapter investigates the application of an intelligent system based on artificial neural network for decision making for Hepatitis. First, datasets are provided for detecting Hepatitis, based on the requirements of artificial neural network inputs and outputs consisting of associated symptoms of each disease as fields of patients' records. Then multilayer perceptron (MLP) artificial neural network is trained to classify Hepatitis disease. In the next sections, details are described.

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