Results 131 to 140 of about 307 (163)

Elman and feedforward neural network based models for predicting mechanical properties of flow formed AA6082 tubes. [PDF]

Sci Rep
De TN, Podder B, Hui NB, Mondal C, Pathak VK.   +4 more
europepmc   +1 more source

Adaptive integration for accurate springback prediction

, 2007
openaire   +1 more source

Development of Viscoplastic Constitutive Model Considering Heating Rate Effect on Grain Size and Phase Evolution in Hot Deformation. [PDF]

Materials (Basel)
Gao Z, Liu S, Lin J, Wang Z, Li D, Zheng K.   +5 more
europepmc   +1 more source

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A Methodology for Springback Prediction

SAE Technical Paper Series, 2001
<div class="htmlview paragraph">The springback of simple geometries can be predicted through theoretical analysis, however problems arise when transferring this analysis to the manufacturing environment. To determine why this is the case, a study of small curvature free bending through theoretical analysis, manufacturing data and Finite Element ...
Mary Gotzinger, S. Kalyanasundaram, M. Cardew-Hall, P. Hodgson   +3 more
openaire   +1 more source

Prediction of springback after bending under tension

International Journal of Material Forming, 2022
published
Yanfeng Yang, Hocine Chalal, Cyrille Baudouin, Gabriela Vincze, Tudor Balan   +4 more
openaire   +2 more sources

Material modelling for accurate springback prediction

International Journal of Material Forming, 2009
The magnitude of the springback depends mainly on the residual stresses in the work piece after the forming stage. An accurate prediction of residual stresses puts, in turn, high demands on the material modelling during the forming simulation. Among the various ingredients that make up the material model, the hardening law is one of the most important ...
Per-Anders Eggertsen, Kjell Mattiasson
openaire   +1 more source

Springback prediction of TWIP automotive sheets

Metals and Materials International, 2009
In an effort to reduce the weight of vehicles, automotive companies are replacing conventional steel parts with light weight alloys and/or with advanced high strength steels (AHSS) such as dual-phase (DP), twinning induced plasticity (TWIP), and transformation induced plasticity (TRIP) steels.
Kanghwan Ahn, Donghoon Yoo, Min Hong Seo, Sung-Ho Park, Kwansoo Chung   +4 more
openaire   +1 more source

Springback Prediction Using Combined Hardening Model

SAE Technical Paper Series, 2000
<div class="htmlview paragraph">The main objective of this paper is to simulate the springback using combined kinematic/isotropic hardening model. Material parameters in the hardening model are identified by an inverse method.</div> <div class="htmlview paragraph">Three-point bending test is conducted on 6022-T4 aluminum sheet.
K. M. Zhao, J. K. Lee
openaire   +1 more source

Springback Prediction in Sheet Forming Simulation

SAE Technical Paper Series, 1994
<div class="htmlview paragraph">Although numerical simulation techniques for sheet metal forming become increasingly maturing in recent years, prediction of springback remains a topic of current investigation. The main point of this paper is to illustrate the effectiveness of a modelling approach where static implicit schemes are used for the ...
Changqing Du, Li Zhang, Neng-Ming Wang
openaire   +1 more source