An artificial-neural-network method for the identification of saturated turbogenerator parameters based on a coupled finite-element/state-space computational algorithm

1995 ◽  
Vol 10 (4) ◽  
pp. 625-633 ◽  
Author(s):  
S.R. Chaudhry ◽  
S. Ahmed-Zaid ◽  
N.A. Demerdash
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
State Space ◽  
Computational Algorithm ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Development of Surrogate Models of Orthopedic Screws to Improve Biomechanical Performance

2012 ◽  
pp. 138-159
Author(s):  
Ching-Chi Hsu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Objective Function ◽  
Bending Strength ◽  
Pullout Strength ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

An optimization approach was applied to improve the design of the lag screws used in double screw nails. However, finite element analyses with an optimal algorithm may take a long time to find the best design. Thus, surrogate methods, either artificial neural networks or multiple linear regressions, were used to substitute for the finite element models. The results showed that an artificial neural network method can accurately develop the objective functions of the lag screws for both the bending strength and the pullout strength. A multiple linear regression method can successfully develop the objective function of the lag screws for the pullout strength, but it failed to construct the objective function for the bending strength. The optimal design of the lag screws could be obtained using the artificial neural network method and genetic algorithms.

Prediction of Friction Coefficients During Scratch Based on an Integrated Finite Element and Artificial Neural Network Method

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Haibo Xie ◽  
Zhanjiang Wang ◽  
Na Qin ◽  
Wenhao Du ◽  
Linmao Qian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Friction Coefficient ◽  
Process Parameters ◽  
Neural Network Method ◽  
Network Method ◽  
Shear Friction ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Abstract An integrated finite element and artificial neural network method is used to analyze the impact of scratch process parameters on some variables related to elastoplastic deformation of titanium alloy. The elastoplastic constitutive parameters applied for scratch simulations are obtained from the nanoindentation experiments and finite element analysis. The validity of the finite element model of scratch is confirmed by comparing the friction forces from simulations to those from experiments. The input parameters of the artificial neural network are three scratch process parameters: tip normal force, tip radius, and shear friction coefficient. The outputs are four variables related to material deformation measured during scratch: scratch depth, elastic recovery height, plowing height, and plowing friction coefficient. The network is trained with pairs of input and output datasets generated by scratch simulations. The prediction results of the neural network are in agreement with the finite element results. The model provides assistance for the prediction and analysis of complex relationships between scratch process parameters and variables related to material deformation, and between the plowing friction coefficient and the relevant parameters. The results show the independence of scratch depth and the shear friction coefficient, and the positive relationships between the shear friction coefficient and plowing friction coefficient.

Sign in / Sign up

Export Citation Format

Share Document