Comparative study of three evolutionary algorithms coupled with neural network model for optimization of electric discharge machining process parameters

2014 ◽  
Vol 229 (9) ◽  
pp. 1504-1516 ◽  
Author(s):  
Arindam Majumder
Keyword(s):  
Neural Network ◽  
Evolutionary Algorithms ◽  
Comparative Study ◽  
Network Model ◽  
Process Parameters ◽  
Electric Discharge ◽  
Neural Network Model ◽  
Machining Process ◽  
Electric Discharge Machining

The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

2010 ◽  
Vol 33 ◽  
pp. 74-78
Author(s):  
B. Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Statistical Regression ◽  
Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

Prediction model for the mechanical properties of compacted poplar powder generated via hot-pressing

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4947-4962
Author(s):  
Jin Yan ◽  
Jianan Liu ◽  
Liqiang Zhang ◽  
Zhili Tan ◽  
Haoran Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Hot Pressing ◽  
Process Conditions ◽  
Wood Powder ◽  
Single Factor ◽  
The Relationship

The influence of the process parameters on the mechanical properties of compact wood powder generated via hot-pressing was analyzed through a single-factor experiment. The mechanical properties exhibited a nonlinear trend relative to the process conditions of hot-pressed compact wood powder. The relationship models between the process parameters and the mechanical properties for the compact wood powder were established by applying a multiple regression analysis and neural network methods combined with data from an orthogonal array design. A comparison between experimental and predicted results was made to investigate the accuracy of the established models by applying several data groups among the single-factor experiments. The results showed that the accuracy of the neural network model in terms of predicting the mechanical properties was greater compared with the multiple regression model. This demonstrates that the established neural network model had a better prediction performance, and it can accurately map the relationship between the process conditions and the mechanical properties of the compact wood powder.

Optimization of electric discharge machining process parameters on AA6351-Al2O3 composites

2020 ◽  
Vol 27 ◽  
pp. 1051-1054
Author(s):  
M. Meignanamoorthy ◽  
M. Ravichandran ◽  
S. Sakthivelu ◽  
S. Dinesh Kumar ◽  
C. Chanakyan ◽  
...  
Keyword(s):  
Process Parameters ◽  
Electric Discharge ◽  
Machining Process ◽  
Electric Discharge Machining

A comparative study of water wall model with a linear model and a neural network model

2014 ◽  
Vol 47 (3) ◽  
pp. 1446-1451
Author(s):  
Un-Chul Moon ◽  
Jaewoo Lim ◽  
Geon Go ◽  
Kwang. Y. Lee
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Linear Model ◽  
Network Model ◽  
Neural Network Model ◽  
Wall Model ◽  
Water Wall
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