Springback prediction in T-section beam bending process using neural networks and finite element method

2013 ◽  
Vol 13 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Y. Song ◽  
Z. Yu
Keyword(s):  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Bending Process ◽  
Beam Bending ◽  
Springback Prediction ◽  
Element Method

A Reverse Approach in Optimizing Pass Parameters

2010 ◽  
Vol 113-116 ◽  
pp. 1707-1711
Author(s):  
Jian Hua Hu ◽  
Yuan Hua Shuang
Keyword(s):  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Learning Process ◽  
Back Propagation ◽  
Fem Simulation ◽  
Steel Pipe ◽  
Good Convergence ◽  
Back Propagation Neural Networks ◽  
Element Method

A method combines a back propagation neural networks (BPNN) with the data obtained using finite element method (FEM) is introduced in this paper as an approach to solve reverse problems. This paper presents the feasibility of this approach. FEM results are used to train the BPNN. Inputs of the network are associated with dimension deviation values of the steel pipe, and outputs correspond to its pass parameters. Training of the network ensures low error and good convergence of the learning process. At last, a group of optimal pass parameters are obtained, and reliability and accuracy of the parameters are verified by FEM simulation.

scholarly journals Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

2017 ◽  
Vol 62 (1) ◽  
pp. 435-442 ◽  
Author(s):  
P. Golewski ◽  
J. Gajewski ◽  
T. Sadowski
Keyword(s):  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Integrated System ◽  
Multilayer Perceptrons ◽  
Optimization Of Geometry ◽  
Thin Walled ◽  
Artificial Neural ◽  
Element Method

Abstract Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. This article presents the application of an integrated system based on artificial neural networks and calculations by the finite element method [FEM] for the optimization of geometry of a thin-walled element of an air structure. To ensure optimal structure, the structure’s geometry was modified by creating side holes and ribs, also with holes. The main criterion of optimization was to reduce the structure’s weight at the lowest possible deformation of the tested object. The numerical tests concerned a fragment of an elevator used in the “Bryza” aircraft. The tests were conducted for networks with radial basis functions [RBF] and multilayer perceptrons [MLP]. The calculations described in the paper are an attempt at testing the FEM - ANN system with respect to design optimization.

Robust Optimization for Tube Bending Process Based on Finite Element Method

2012 ◽  
Vol 531-532 ◽  
pp. 746-750
Author(s):  
Xue Wen Chen ◽  
Ze Hu Liu ◽  
Jing Li Zhang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Robust Design ◽  
Process Parameters ◽  
Design Method ◽  
Design Parameters ◽  
Tube Bending ◽  
Performance Variation ◽  
Bending Process ◽  
Element Method

The main causes of performance variation in tube bending process are variations in the mechanical properties of material, initial tube thickness, coefficient of friction and other forming process parameters. In order to control this performance variation and to optimize the tube bending process parameters, a robust design method is proposed in this paper for the tube bending process, based on the finite element method and the Taguchi method. During the robust design process, the finite element analysis is incorporated to simulate the tube bending process and calculate the objective function value, the orthogonal design method is selected to arrange the simulation experiments and calculate the S/N ratio. Finally, a case study for the tube bending process is implemented. With the objective to control tube crack (reduce the maximum thinning ratio) and its variation, the robust design mathematical model is established. The optimal design parameters are obtained and the maximum thinning ratio has been reduced and its variation has been controlled.

Springback Prediction Compensation and Optimization for Front Side Member in Sheet Metal Forming Using FEM Simulation

2013 ◽  
Vol 789 ◽  
pp. 436-442
Author(s):  
Agus Dwi Anggono ◽  
Waluyo Adi Siswanto ◽  
Omar Badrul
Keyword(s):  
Numerical Simulation ◽  
Finite Element Method ◽  
Finite Element ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Fem Simulation ◽  
Element Size ◽  
Springback Prediction ◽  
Element Method

Numerical simulation by finite element method has become a powerful tool in predicting and preventing the unwanted effects of sheet metals technological processing. One of the most important problems in sheet metal forming is the compensation of springback. To improve the accuracy of the formed parts, the die surfaces are required to be optimized so that after springback the geometry falls at the expected shape. This paper presents and discusses numerical simulation procedure of die compensation by using the methods of Simplified Displacement Adjustment (SDA). This analysis use Benchmark 3 models of Numisheet 2011. Sensitively analysis was done by using finite element method (FEM) show that the springback values are influenced by element size, integration points and material properties.

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