English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The objective of this study is to predict influences of tooling parameters such as die and punch radius, blank holder force and friction coefficient between the die and the blank surfaces in a deep drawing process on the wrinkling height in aluminium AA5754 by using the response surface methodology (RSM) and an artificial neural network (ANN). The 3D finite element method (FEM), i.e. the Abaqus software, is employed to model the deep drawing process. In order to investigate the accuracy of this model, the results are compared with experimental results. The data derived from the FEM are used for modelling the RSM and training an ANN. Finally, the RSM and ANN outputs are compared so as to select the best model. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results.

Wrinkling Prediction in Deep Drawing by Using Response Surface Methodology and Artificial Neural Network