The authors used three different methods of statistical data analysis to establish correlations between the water contact angle (CA) on ductile iron and composition, roughness (grit size), elapsed time between sample preparation and CA measurement and droplet size. The three methods are linear regression analysis (LRA), artificial neural network (ANN) model and multivariate polynomial regression analysis. It was established that the size of the water droplet is statistically insignificant, while correlations with the other three parameters were found. Surface roughness is the most important predictor of CA. A low coefficient of determination of the linear regression indicates that the correlation is non-linear. The ANN model showed much stronger predictive potential than LRA. The authors discuss the correlation with the experimental values of the CA and the physical mechanisms behind the observed trends. It is particularly promising that the ANN can be trained to predict the wetting characteristics. The application of machine-learning methods to synthesize new materials and coatings with desired surface properties, such as self-cleaning, is a technology that may become part of the emergent ‘triboinformatics’ field, related to the application of machine-learning methods to surface science and engineering.

中文翻译：

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机器学习方法来预测铁基复合材料的润湿性能

作者使用三种不同的统计数据分析方法来建立球墨铸铁上的水接触角（CA）与成分，粗糙度（粒度），样品制备与CA测量之间的经过时间以及液滴尺寸之间的相关性。这三种方法分别是线性回归分析（LRA），人工神经网络（ANN）模型和多元多项式回归分析。已经确定，水滴的大小在统计学上不显着，而发现与其他三个参数的相关性。表面粗糙度是CA最重要的预测指标。线性回归的确定系数低表明相关性是非线性的。ANN模型显示出比LRA强得多的预测潜力。作者讨论了与CA实验值的相关性以及观察到的趋势背后的物理机制。特别有希望的是，可以训练ANN来预测润湿特性。机器学习方法用于合成具有所需表面特性（例如自清洁）的新材料和涂层的技术，可能会成为新兴的“摩擦信息学”领域的一部分，这与将机器学习方法应用于表面相关科学与工程。