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Springback Prediction in Sheet Metal Forming, Based on Finite Element Analysis and Artificial Neural Network Approach

Computational Mechanics and Optimization Lab, School of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece
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Appl. Mech. 2020, 1(2), 97-110; https://doi.org/10.3390/applmech1020007
Received: 17 January 2020 / Revised: 4 March 2020 / Accepted: 17 March 2020 / Published: 6 April 2020
Sheet metal forming is one of the most important manufacturing processes applied in many industrial sectors, with the most prevalent being the automotive and aerospace industries. The main purpose of that operation is to produce a desired formed shape blank, without any material failures, which should lie well within the acceptable tolerance limits. Springback is affected by factors such as material properties, sheet thickness, forming tools geometry, contact and friction, etc. The present paper proposes a novel neural network system for the prediction of springback in sheet metal forming processes. It is based on Bayesian regularized backpropagation networks, which have not been tested in the literature, according to the authors’ best knowledge. For the creation of training examples a carefully prepared Finite Element model has been created and validated for a test case used in similar industrial studies. View Full-Text
Keywords: FEA; S-Rail; sheet metal forming; springback; artificial neural networks FEA; S-Rail; sheet metal forming; springback; artificial neural networks
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Spathopoulos, S.C.; Stavroulakis, G.E. Springback Prediction in Sheet Metal Forming, Based on Finite Element Analysis and Artificial Neural Network Approach. Appl. Mech. 2020, 1, 97-110.

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