Validation of Non-linear Load-Controlled CAE Analyses of Oil-Canning Tests of Hood and Door Assemblies

2003-01-0603

03/03/2003

Event
SAE 2003 World Congress & Exhibition
Authors Abstract
Content
Two finite element methodologies for simulating oil-canning tests on closure assemblies are presented. Reflecting the experimental conditions, the simulation methodologies assume load-controlled situations. One methodology uses an implicit finite-element code, namely ABAQUS®, and the other uses an explicit code, LS-DYNA®. It is shown that load-displacement behavior predicted by both the implicit and explicit codes agree well with experimental observations of oil-canning in a hood assembly. The small residual dent depth predictions are in line with experimental observations. The method using the implicit code, however, yields lower residual dent depth than that using the explicit code. Because the absolute values of the residual dent depths are small in the cases examined, more work is needed, using examples involving larger residual dent depth, to clearly distinguish between the two procedures. The analysis performed using the implicit code was significant more efficient (in terms of CPU hours) than the analysis done using the explicit code. The effects of forming strains are qualitatively examined. Forming induced thickness changes and plastic strains may not have a significant effect on oil-canning behavior, but may influence the residual dent depth strongly. Further reinforcement of the predictability of the methodology is demonstrated by an oil-canning simulation, using the implicit code, of a door assembly.
Meta TagsDetails
DOI
https://doi.org/10.4271/2003-01-0603
Pages
9
Citation
Iyengar, R., Chang, T., Zhao, Y., Singri, M. et al., "Validation of Non-linear Load-Controlled CAE Analyses of Oil-Canning Tests of Hood and Door Assemblies," SAE Technical Paper 2003-01-0603, 2003, https://doi.org/10.4271/2003-01-0603.
Additional Details
Publisher
Published
Mar 3, 2003
Product Code
2003-01-0603
Content Type
Technical Paper
Language
English