Kim Sung Wook, Kang Seong-Hoon, Kim Se-Jong, Lee Seungchul
Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang, Republic of Korea.
Korea Institute of Materials Science, 797 Changwon-daero, Seongsan-gu, Changwon, Republic of Korea.
Sci Rep. 2021 Mar 15;11(1):5902. doi: 10.1038/s41598-021-85407-y.
Advanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.
先进高强度钢（AHSS）是一种具有多相微观结构的钢，它在多种条件下进行加工，以满足当前行业的高性能要求。深度神经网络（DNN）已成为材料科学中一种很有前景的工具，用于估计这些钢的相体积分数。尽管它有优点，但其主要缺点之一是需要向网络提供足够数量的带有正确标签的训练数据。在许多领域，获取数据并对其进行标注是极其耗费人力的，这往往是一个挑战。为了克服这一挑战，提出了一种无监督学习DNN的方法，该方法不需要任何人工标注。信息最大化生成对抗网络（InfoGAN）用于学习每个相的潜在概率分布，并生成带有类别标签的真实样本点。然后，将生成的数据用于训练MLP分类器，该分类器进而预测原始数据集的标签。结果表明，最大平均相对误差为4.53%，而最低可达0.73%，这意味着估计的相分数与真实相分数非常接近。这表明所提出的方法在工业和学术界快速精确估计相体积分数方面具有很高的可行性。
