Abstract
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.
Highlights
Sheet metal forming is widely used in many applications but, among others, it is mainly utilized in the automotive and aerospace industries
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
The elicited results from deformation and springback simulations are compared with the corresponding outcomes from reference [13], during which the performance of a proposed solid-shell element is investigated in the framework of deep drawing simulations and springback predictions
Summary
Sheet metal forming is widely used in many applications but, among others, it is mainly utilized in the automotive and aerospace industries. Marretta et al (2010) [9] and Prates et al (2018) [10] performed numerical studies on springback prediction in rail-shaped sheet components, under the variability of material properties, using FEA coupled with RSM metamodeling techniques. Dib et al (2018, 2019) [11,12] developed a Machine Learning-based approach to predict the occurrence of springback in sheet metal forming processes, under the variability of material properties and process parameters. A novel neural network system for the prediction of springback in sheet metal forming processes is proposed. It is mainly based on Bayesian regularized backpropagation networks, which have not been tested in the literature, according to the authors’ best knowledge. Density [g/cm3] Young Modulus [N/mm2] Poisson ratio Initial yield stress, [N/mm2] Max change in size of elastic range, Q [N/mm2] Rate of change of elastic range size, β
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