Necking plays a central role in the challenge of accurately describing the mechanical behavior, particularly hardening, of ductile materials at large strains. However, the evolution of the necked zone remains poorly understood. This study elucidates the nucleation, onset, and shape evolution of the necked zone in a 6061 aluminum alloy sheet under uniaxial stretching via a deep learning approach. A DeepLabv3 + image segmentation model is developed to accurately define the real-time shape of the necked zone. Fundamental parameters characterizing the necked zone are calculated through geometric analysis of segmented images. The evolution of these parameters is examined and compared to delineate the changing properties of the necked zone and its relationship with evolving stress. The study reveals that strain concentration zones emerging along the specimen boundary prior to yielding are prone to evolve into the final necking and fracture sites. This finding suggests the potential for early prediction of necking location. After a long phase of necking nucleation, the bifurcation point of stress appears earlier than the peak load point predicted by the famous Considère’s criterion. These results introduce a novel approach for identifying necking events and present new insights into the necking phenomenon and its description. Additionally, these findings propose a new way to identify the representative constitutive relationship of ductile materials at large strains.
扫码关注我们
求助内容:
应助结果提醒方式:
