Chongqing Creation Vocational College, Yongchuan, 402160, Chongqing, China.
College of Applied Technology, Dalian Ocean University, Dalian, 116300, Liaoning, China.
Sci Rep. 2023 Feb 22;13(1):3115. doi: 10.1038/s41598-023-28719-5.
Effect of microstructure on the formability of the stainless sheet metals is a major concern for engineers in sheet industries. In the case of austenitic steels, existence of strain-induced martensite ([Formula: see text]-martensite) in their micro structure causes considerable hardening and formability reduction. In the present study, we aim to evaluate the formability of AISI 316 steels with different intensities of martensite via experimental and artificial intelligence methods. In the first step, AISI 316 grade steels with 2 mm initial thicknesses are annealed and cold rolled to various thicknesses. Subsequently, the relative area of strain-induced martensite are measured using metallography tests. Formability of the rolled sheets are determined using hemisphere punch test to obtain forming limit diagrams (FLDs). The data obtained from experiments were further utilized to train and validate an artificial neural fuzzy interfere system (ANFIS). After training the ANFIS, predicted major strains by the neural network are compared to a new set experimental results. The results indicate that cold rolling has unfavorable effects on the formability of this type of stainless steels while significantly strengthens the sheets. Moreover, the ANFIS exhibits satisfactory results in comparison to the experimental measurements.
微观结构对不锈钢板材成形性的影响是板材行业工程师关注的主要问题。在奥氏体钢的情况下，其微观结构中存在应变诱发马氏体([Formula: see text]-马氏体)会导致显著的硬化和成形性降低。在本研究中，我们旨在通过实验和人工智能方法评估具有不同马氏体强度的 AISI 316 钢的成形性。在第一步中，将初始厚度为 2mm 的 AISI 316 级钢退火并冷轧至不同的厚度。随后，使用金相试验测量应变诱发马氏体的相对面积。通过半球冲头试验确定轧制薄板的成形性，以获得成形极限图（FLD）。进一步利用实验获得的数据来训练和验证人工神经网络模糊干涉系统（ANFIS）。在训练 ANFIS 后，将神经网络预测的主要应变与新的一组实验结果进行比较。结果表明，冷轧对这种类型的不锈钢的成形性有不利影响，同时显著增强了板材的强度。此外，与实验测量相比，ANFIS 显示出令人满意的结果。
