ABSTRACT

ZnNi alloy coating is commonly used to enhance the corrosion resistance of steel. The percentage of Ni should be maintained between 12% and 14% in the coating for best corrosion performance. The response surface design (RSD), polynomial regression (PR), support vector regression (SVR), XGBoost regression (XGB), K-nearest neighbours regression and Gaussian process regression (GP) algorithms have been used to predict the ZnNi alloy coating thickness and Ni % amount in the coating. As statistical indices mean square error (MSE) and correlation coefficient (R²) were used to compare the models. The results of the analysis show that the XGB algorithm gives the best estimation for both ZnNi thickness and Ni%. A high correlation was observed between the predicted values and experimental results. R² values of 0.87 and 0.81 were acquired for ZnNi thickness and Ni %, respectively, using the XGB algorithm. This study has proved that the machine learning algorithm is a promising method to predict the ZnNi coating thickness and Ni % in the alloy based on the composition of the ZnNi electroplating bath.

摘要

ZnNi合金涂层通常用于增强钢的耐腐蚀性。为了获得最佳的腐蚀性能，镀层中的镍百分比应保持在12％至14％之间。响应面设计（RSD），多项式回归（PR），支持向量回归（SVR），XGBoost回归（XGB），K近邻回归和高斯过程回归（GP）算法已用于预测ZnNi合金涂层的厚度镀层中的Ni％含量。作为统计指标，均方误差（MSE）和相关系数（R ²）用于比较模型。分析结果表明，XGB算法对ZnNi厚度和Ni％都给出了最佳估计。在预测值和实验结果之间观察到高度相关性。使用XGB算法分别获得的ZnNi厚度和Ni％的R ²值分别为0.87和0.81。这项研究证明，机器学习算法是一种基于ZnNi电镀液成分预测合金中ZnNi涂层厚度和Ni％的有前途的方法。