Knowledge based cloud FE simulation of sheet metal forming processes

Title: Knowledge based cloud FE simulation of sheet metal forming processes
Authors: Wang, L
Zhou, D
Gao, H
Wang, A
Liu, J
El Fakir, O
Politis, DJ
Wang, L
Lin, J
Item Type: Journal Article
Abstract: The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques. This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions.
Issue Date: 13-Dec-2016
Date of Acceptance: 6-Jan-2016
ISSN: 1940-087X
Publisher: Journal of Visualized Experiments (JoVE)
Journal / Book Title: Journal of Visualized Experiments
Volume: 118
Copyright Statement: © 2016 Creative Commons Attribution 3.0 License (
Sponsor/Funder: Commission of the European Communities
Technology Strategy Board
Funder's Grant Number: NMP3-SE-2013-604240
Publication Status: Published
Article Number: e53957
Appears in Collections:Faculty of Engineering
Mechanical Engineering

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