In-depth online monitoring of the sheet metal process state derived from multi-scale simulations

Senn, Melanie; Jöchen, Katja; Van, Tung Phan; Böhlke, Thomas; Link, Norbert

doi:10.1007/s00170-013-4833-0

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…12 Additionally, numerous previous studies indicate, that microstructural information like crystallographic texture can affect the macroscopic quantities like springback of sheet metals. [13][14][15] However, classical phenomenological models (as e.g. the Barlat et al yield criteria) 16 capture in plane anisotropy but do not allow the incorporation of microstructure information, thus prohibiting direct usage of experimentally accessible data.…”

Section: Introductionmentioning

confidence: 99%

Improvement of process control in sheet metal forming by considering the gradual properties of the initial sheet metal

Zettl

Schmid

Pulvermacher

et al. 2023

The Journal of Strain Analysis for Engineering Design

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In scientific studies, sheet metal is usually considered as a two-dimensional body. Thus, it is accepted that material properties are in most cases regarded homogeneous in thickness direction. However, a gradation of certain properties becomes apparent when going beyond the standard characterization methods for sheet metals, which can for example, influence the springback behavior and the thinning of the sheet after forming. Thus, the aim of this work is to further improve the prediction accuracy of springback after forming in simulations, by implementing several inhomogeneous properties over the sheet thickness in an existing material model. For this purpose, the entire procedure from the identification of the inhomogeneous properties for describing the gradation to the implementation in a numerical model and its validation by comparing experimental and simulated bending operations is carried out on a DC04 cold-forming steel in order to prove its influence on the springback behavior. It is shown that including graded material properties in simulations does indeed have an impact on the prediction quality of springback and that the information about inhomogeneous properties can be provided by existing characterization methods with a high local resolution like electron backscatter diffraction or X-ray stress analysis. In a further step, it was possible to validate the improvement in numerical accuracy by comparing the prediction of the springback angle from both the existing and the extended model with experimental bending results. Both the initial model as well as the model supplemented with the 3D properties provide a good prediction accuracy in the solution heat treated material state. For the predeformed material, however, the initial numerical model predicts a springback angle of about 13°, which deviates remarkably from the experimentally obtained mean value of about 17°. The extended model delivers a significantly improved accuracy in springback prediction in relation to the initial prediction (deviation of 4°) with a minor deviation of only about 0.8°, which proves the importance of considering the gradation of material properties in thickness direction for an overall higher dimensional accuracy of sheet metal products.

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Section: Introductionmentioning

confidence: 99%

Improvement of process control in sheet metal forming by considering the gradual properties of the initial sheet metal

Zettl

Schmid

Pulvermacher

et al. 2023

The Journal of Strain Analysis for Engineering Design

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Schwarz et al used principle component analysis [4] and Fourier-based transformation methods [5] for dimension reduction with subsequent linear regression to predict physical fields of different sheet metal forming processes. Furthermore, Senn et al [6] used a neuronal network to predict physical fields of a cup drawing process in dependency of the current time step. Moreover, Pfrommer et al [7] proposed a deep multilayer perceptron as a metamodel for process optimization in manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Metamodeling of a deep drawing process using conditional Generative Adversarial Networks

Link

Bodenstab

Penter

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Optimization tasks as well as quality predictions for process control require fast responding process metamodels. A common strategy for sheet metal forming is building fast data driven metamodels based on results of Finite Element (FE) process simulations. However, FE simulations with complex material models and large parts with many elements consume extensive computational time. Hence, one major challenge in developing metamodels is to achieve a good prediction precision with limited data, while these predictions still need to be robust against varying input parameters. Therefore, the aim of this study was to evaluate if conditional Generative Adversarial Networks (cGAN) are applicable for predicting results of FE deep drawing simulations, since cGANs could achieve high performance in similar tasks in previous work. This involves investigations of the influence of data required to achieve a defined precision and to predict e.g. wrinkling phenomena. Results show that the cGAN used in this study was able to predict forming results with an averaged absolute deviation of sheet thickness of 0.025 mm, even when using a comparable small amount of data.

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In-depth online monitoring of the sheet metal process state derived from multi-scale simulations

Cited by 2 publications

References 29 publications

Improvement of process control in sheet metal forming by considering the gradual properties of the initial sheet metal

Improvement of process control in sheet metal forming by considering the gradual properties of the initial sheet metal

Metamodeling of a deep drawing process using conditional Generative Adversarial Networks

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