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使用自适应神经模糊推理系统(ANFIS)研究冷轧过程中减薄量对板材成形性的影响。

Effects of thickness reduction in cold rolling process on the formability of sheet metals using ANFIS.

机构信息

College of Humanities and Education, Xijing University, Xi'an, 710123, Shaanxi, People's Republic of China.

Shanghai Jianqiao University, Shanghai, 201306, People's Republic of China.

出版信息

Sci Rep. 2022 Jun 21;12(1):10434. doi: 10.1038/s41598-022-13694-0.

DOI:10.1038/s41598-022-13694-0
PMID:35729162
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9213415/
Abstract

Cold rolling has detrimental effect on the formability of sheet metals. It is, however, inevitable in producing sheet high quality surfaces. The effects of cold rolling on the forming limits of stretch sheets are not investigated comprehensively in the literature. In this study, a through experimental study is conducted to observe the effect of different cold rolling thickness reduction on the formability of sheet metals. Since the experimental procedure of such tests are costly, an artificial intelligence is also adopted to predict effects of cold thickness reduction on the formability of the sheet metals. In this regard, St14 sheets are examined using tensile, metallography, cold rolling and Nakazima's hemi-sphere punch experiments. The obtained data are further utilized to train and test an adaptive neural network fuzzy inference system (ANFIS) model. The results indicate that cold rolling reduces the formability of the sheet metals under stretch loading condition. Moreover, the tensile behavior of the sheet alters considerably due to cold thickness reduction of the same sheet metal. The trained ANFIS model also successfully trained and tested in prediction of forming limits diagrams. This model could be used to determine forming limit strains in other thickness reduction conditions. It is discussed that determination of forming limit diagrams is not an intrinsic property of a chemical composition of the sheet metals and many other factors must be taken into account.

摘要

冷轧对板材的成形性有不利影响。然而,在生产高质量板材表面时,冷轧是不可避免的。文献中并没有全面研究冷轧对拉伸板材成形极限的影响。在本研究中,通过实验研究观察了不同冷轧减薄厚度对板材成形性的影响。由于此类测试的实验过程成本高昂,因此还采用了人工智能来预测冷轧厚度对板材成形性的影响。在这方面,使用拉伸、金相、冷轧和 Nakazima 半球冲头实验对 St14 板材进行了检查。进一步利用获得的数据来训练和测试自适应神经网络模糊推理系统 (ANFIS) 模型。结果表明,冷轧会降低拉伸加载条件下板材的成形性。此外,由于同一板材的冷轧减薄,板材的拉伸行为会发生很大变化。经过训练的 ANFIS 模型也成功地用于预测成形极限图。该模型可用于确定其他减薄条件下的成形极限应变。讨论表明,成形极限图的确定不是板材化学成分的固有特性,必须考虑许多其他因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/1320eef76225/41598_2022_13694_Fig13_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/fc52f5ce663e/41598_2022_13694_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/e547633b9540/41598_2022_13694_Fig10_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/04e713e9aaf3/41598_2022_13694_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/437e77b975b0/41598_2022_13694_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/18eef4266fef/41598_2022_13694_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/bbaf0a9ad866/41598_2022_13694_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/a31a5fd2cb0d/41598_2022_13694_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/8177727cb7a2/41598_2022_13694_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/f88ff95d6d74/41598_2022_13694_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/faf17cc96ede/41598_2022_13694_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/fc52f5ce663e/41598_2022_13694_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/e547633b9540/41598_2022_13694_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/b107835130dd/41598_2022_13694_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/ce43897d20e9/41598_2022_13694_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df1/9213415/1320eef76225/41598_2022_13694_Fig13_HTML.jpg

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引用本文的文献

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