MATEC Web Conf.
Volume 343, 202110th International Conference on Manufacturing Science and Education – MSE 2021
|Number of page(s)||6|
|Section||Advanced Manufacturing Technologies|
|Published online||04 August 2021|
Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction
Mechanical Engineering Laboratory, Sidi Mohamed Ben Abdellah university, Fez, Morocco
2 Industrial Techniques Laboratory, Sidi Mohamed Ben Abdellah university, Fez, Morocco
* Iliass El Mrabti: firstname.lastname@example.org
In sheet metal manufacturing, the ability to predict failures, such as springback, wrinkling and thinning, are of high importance. The objective of this study is to compare the response surface methodology (RSM) and the artificial neural network (ANN) model for predicting springback during the deep drawing process. In this investigation, friction coefficient, punch speed and blank holder force were considered as input variables. Sample data were planned by the complete factorial design and obtained via numerical simulation. To compare the RSM and ANN models, a goodness of-fit test was performed. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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