Kim Sung-Hee, Jeong Chanyoung
Department of Industrial ICT Engineering, Dong-eui University, 176 Eomgwang-ro, Busanjin-gu, Busan 47340, Korea.
Department of Advanced Materials Engineering, Dong-eui University, 176 Eomgwang-ro, Busanjin-gu, Busan 47340, Korea.
Materials (Basel). 2021 Feb 26;14(5):1089. doi: 10.3390/ma14051089.
This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide () nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in nanostructures' thicknesses by performing anodization. We successfully grew films with different thicknesses by one-step anodization in ethylene glycol containing and at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of . As the characteristics of changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.
本研究旨在证明应用八种机器学习算法来预测不同阳极氧化工艺下二氧化钛（TiO₂）纳米结构表面特征分类的可行性。我们总共制备了100个样品，并通过阳极氧化评估了纳米结构厚度的变化。我们在含有不同添加剂且施加电压差范围为10 V至100 V的乙二醇中，通过一步阳极氧化在不同的阳极氧化持续时间下成功生长出了不同厚度的薄膜。我们发现TiO₂纳米结构的厚度在时间差异下取决于阳极氧化电压。因此，我们测试了应用机器学习算法来预测TiO₂变形的可行性。由于TiO₂的特性根据不同的实验条件而变化，我们将其表面孔隙结构分为两类和四组。对于基于粒度的分类，我们评估了层的形成、粗糙度、孔隙的形成和孔隙高度。我们应用八种机器学习技术来预测二元和多类分类。对于二元分类，随机森林和梯度提升算法具有相对较高的性能。然而，所有八种算法的得分都高于0.93，这表明在估计孔隙存在方面具有较高的预测性。相比之下，决策树和三种集成方法在多类分类中具有相对较高的性能，准确率大于0.79。在二元和多类分类中使用的最弱算法是k近邻算法。我们相信这些结果表明我们可以应用机器学习技术来预测表面质量的改善，从而带来智能制造技术以更好地控制颜色外观、超疏水性、超亲水性或更好的效率。
