Computing Method and Hardware Circuit Implementation of Neural Network on Finite Element Analysis

Neural network representation of finite element method

Simulation and modeling of friction welding of stainless steel to aluminum alloy using finite element method and artificial neural network

Aluminum to stainless steel joints are broadly used in industries in order to reduce fuel consumption. While fusion welding is not a suitable method to join these metals. solid state welding, like friction welding (FW), is an effective way to this process. However, risk of intermetallic compounds (IMCs) formation is probable in these welds. In previews investigations formation of FeAl3, Fe2Al5 ...

Simulation and modeling of friction welding of stainless steel to aluminum alloy using finite element method and artificial neural network

Aluminum to stainless steel joints are broadly used in industries in order to reduce fuel consumption. While fusion welding is not a suitable method to join these metals. solid state welding, like friction welding (FW), is an effective way to this process. However, risk of intermetallic compounds (IMCs) formation is probable in these welds. In previews investigations formation of FeAl3, Fe2Al5 ...

Evaluation of Ultimate Torsional Strength of Reinforcement Concrete Beams Using Finite Element Analysis and Artificial Neural Network

Due to lack of theory of elasticity, estimation of ultimate torsional strength of reinforcement concrete beams is a difficult task. Therefore, the finite element methods could be applied for determination of strength of concrete beams. Furthermore, for complicated, highly nonlinear and ambiguous status, artificial neural networks are appropriate tools for prediction of behavior of such states. ...

Finite Precision Error Analysis of Neural Network Hardware Implementations

29 computation. On the other hand, for network learning, at least 14-16 bits of precision must be used for the weights to avoid having the training process divert too much from the trajectory of the high precision computation. References [1] D. Hammerstrom. A VLSI architecture for high-performance, low cost, on-chip learning. Figure 10: The average squared dierences between the desired and actu...