Microscale modeling of the ductile fracture behavior of thin stainless steel sheets

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Firouzjaei, MK (Author) Other; Naeini, HM (Author) Other; Kasaei, MM (Author) Other; Mirnia, MJ (Author) Other; da Silva, LFM (Author) FEUP

Journal

Title: Thin-Walled Structures

Vol. 196

ISSN: 0263-8231

Publisher: Elsevier

Indexing

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Other information

Authenticus ID: P-00Z-EQA

DOI: 10.1016/j.tws.2023.111457

Abstract (EN): This study aims to model the fracture behaviour of thin stainless steel sheets in the microscale, which are widely used in the manufacturing of thin-walled structures such as bipolar plates, while considering the effects of geometry and grain size. To achieve this, 304 austenitic stainless steel with two different thicknesses is heat-treated to obtain samples with distinctive grain sizes. Uniaxial tensile tests and cup drawing tests are performed on the resulting samples, and the fracture strains are measured using a digital image correlation system. The morphology of fracture surfaces is also analysed to understand fracture mechanisms in the microscale. A new ductile fracture model based on the normalized Cockcroft-Latham criterion is developed to take the size effect into account, which is then applied in finite element analysis to predict damage evolution and fracture initiation during the tests. The results reveal a significant reduction in the fracture strain with decreasing sheet thickness and increasing grain size. Furthermore, the fracture mode changed from tensile fracture of a polycrystalline metal to shear fracture of a single-crystal metal as the number of grains across the thickness decreased. It is confirmed that the proposed model accurately replicates the decrease of the fracture strain as the plastic deformation scaled down to the microscale and successfully predicts the displacement at the onset of fracture under different loading conditions. Based on these results, it can be concluded that the proposed model has great potential for predicting fracture in microforming processes.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17