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Research Article

Prediction of forming limit diagrams for steel sheets with an artificial neural network and comparison with empirical and theoretical models

Cengiz Görkem Dengiz, Fevzi Şahin

Department of Mechanical Engineering, Ondokuz Mayis University, Samsun, Turkiye

Keywords

Abstract


Formability; 

Artificial neural network; 

 Forming limit diagram; 

Steel sheets; 

Sheet metal forming



The automotive industry heavily relies on forming limit diagrams (FLDs) as essential tools for ensuring the quality and manufacturability of sheet metal components. However, accurately determining FLDs can be complex and resource-intensive due to the numerous material properties and variables involved. To address this challenge, this research employs an artificial neural network (ANN) model to predict FLDs for sheet metals, explicitly focusing on the automotive sector. The study begins by gathering material properties, including sheet thickness, yield strength, ultimate tensile strength, uniform elongation, hardening exponent, and strength coefficient. These properties serve as crucial inputs for the ANN model. Sensitivity analysis is then conducted to discern how each parameter influences FLD predictions. The ANN model is meticulously constructed, with a 6-15-22-3 structure, and subsequently trained to predict FLDs. The results are promising, as the model achieves an exceptional R-value of 0.99995, indicating high accuracy in its predictions. Comparative analysis is carried out by pitting the ANN-generated FLDs against experimental data. The findings reveal that the ANN model predicts FLDs with remarkable precision, exhibiting only a 3.4% difference for the FLD0 value. This level of accuracy is particularly significant in the context of automotive manufacturing, where even minor deviations can lead to substantial product defects or manufacturing inefficiencies. It offers a swift and reliable way of predicting FLDs, which can be instrumental in optimising manufacturing processes, reducing material waste, and ensuring product quality. In conclusion, this research contributes to the automotive manufacturing sector by providing a robust and efficient method for predicting FLDs.

© 2023 MIM Research Group. All rights reserved.

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2023 Reviewer Awards:

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2023 Best Paper Award:

The paper authored by Ferzan Fidan, Naim Aslan, Mümin Mehmet Koç entitled as “Morpho-structural and compressive mechanical properties of graphene oxide reinforced hydroxyapatite scaffolds for bone tissue applications” is awarded.



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The paper authored by Ercan Işık, Ehsan Harirchian, Hüseyin Bilgin, Kirti Jadhav entitled as “The effect of material strength and discontinuity in RC structures according to different site-specific design spectra" is awarded.


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