Xia Jiaping, Won Chanhee, Kim Hyunggyu, Lee Wonjoo, Yoon Jonghun
Department of Mechanical Design Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea.
Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Korea.
Materials (Basel). 2022 Feb 24;15(5):1714. doi: 10.3390/ma15051714.
This paper mainly proposes two kinds of artificial neural network (ANN) models for predicting the plastic anisotropy properties of sheet metal using spherical indentation test, which minimizes measurement time, costs, and simplifies the process of obtaining the anisotropy properties than the conventional tensile test. The proposed ANN models for predicting anisotropic properties can replace the traditional complex dimensionless analysis. Moreover, this paper is not limited to the prediction of yield strength anisotropy but also further accurately predicts the Lankford coefficient in different orientations. We newly construct an FE spherical indentation model, which is suitable for sheet metal in consideration of actual compliance. To obtain a large dataset for training the ANN, the constructed FE model is utilized to simulate pure and alloyed engineering metals with one thousand elastoplastic parameter conditions. We suggest the specific variables of the residual indentation mark as input parameters, also with the indentation load-depth curve. The profile of the residual indentation, including the height and length in different orientations, are used to analyze the anisotropic properties of the material. Experimental validations have been conducted with three different sheet alloys, TRIP1180 steel, zinc alloy, and aluminum alloy 6063-T6, comparing the proposed ANN model and the uniaxial tensile test. In addition, machine vision was used to efficiently analyze the residual indentation marks and automatically measure the indentation profiles in different orientations. The proposed ANN model exhibits remarkable performance in the prediction of the flow curves and Lankford coefficient of different orientations.
本文主要提出了两种人工神经网络（ANN）模型，用于通过球形压痕试验预测金属板材的塑性各向异性性能。与传统拉伸试验相比，该方法可最大限度地减少测量时间和成本，并简化获取各向异性性能的过程。所提出的用于预测各向异性性能的ANN模型可以取代传统的复杂无量纲分析。此外，本文不仅限于预测屈服强度各向异性，还能进一步准确预测不同方向的兰克福德系数。我们新构建了一个有限元球形压痕模型，该模型考虑了实际柔度，适用于金属板材。为了获得用于训练ANN的大型数据集，利用所构建的有限元模型在一千种弹塑性参数条件下模拟纯工程金属和合金工程金属。我们建议将残余压痕标记的特定变量作为输入参数，同时结合压痕载荷-深度曲线。残余压痕的轮廓，包括不同方向的高度和长度，用于分析材料的各向异性性能。已对三种不同的板材合金TRIP1180钢、锌合金和6063-T6铝合金进行了实验验证，比较了所提出的ANN模型和单轴拉伸试验。此外，利用机器视觉有效地分析了残余压痕标记，并自动测量了不同方向的压痕轮廓。所提出的ANN模型在预测不同方向的流动曲线和兰克福德系数方面表现出卓越的性能。
