通过化学分析预测锌基涂层耐蚀性能的专家系统

Expert systems for prediction of corrosion properities of Zn-based coatings from the chemical analysis

本研究旨在开发一种用于预测锌基涂层耐腐蚀性的通用方法,其可以表示为加速盐雾试验中的总质量损失。本方法仅基于三个分析参数,即锌、铝和镁的总涂层质量。这种限制的原因是这三种参数可能通过在线分析获得。然后,预测的耐腐蚀性被包括在一个过程/质量控制系统。加速腐蚀试验在布雷斯特的Swerea KIMAB IC(腐蚀研究所)以及比利时的冶金研究中心(CRM)进行。试验按照雷诺ECC1试验D172028/--C(12周)以及CRM研发的加速循环腐蚀试验进行。根据总质量损失情况,原材料被分为四个耐腐蚀级别。所有腐蚀试验都清晰、充分地说明了元素镁和铝的正面影响。对于涂层中大多数这些元素来说,元素镁和铝的影响比单独元素锌的影响大很多。因此,引入了一个新的量,叫做"等量元素锌涂层重量"。此量与锌、铝和镁的涂层重量线性相关。使用专家系统开发了一种用于预测耐腐蚀性的模型,此模型基于回归分析和"决策树"算法。根据上述提及的三个分析参数(即锌、铝和镁的总涂层质量),可以使用开发的模型准确对27种材料中的25种进行分类。总之,即使是在线状态,这种方法也有可能准确预测出腐蚀行为。出于材料研发的目的,还扩展了专家系统使其包括其他分析参数。

The purpose of the work is to develop a general method,to predict the corrosion resistance of Zn-based coatings,expressed as total mass loss in an accelerated salt spray test.The method is to be based on just three analytical parameters;the total coating weights of Zn,Al and Mg.The reason for this restriction is that determination of these three parameters is possible in on-line analysis.The predicted corrosion resistance could then be included in a process/quality control system.Accelerated corrosion tests have been carried out by Swerea KIMAB IC（Institut de Corrosion） in Brest,and CRM in Belgium.Test were run according to the Renault ECC1 test D172028/--C（12 weeks）,and with an accelerated cyclic corrosion test developed by CRM.The materials were divided into four corrosion classes according to total mass loss.All corrosion experiments show clearly the well documented positive influence of magnesium and aluminium.In relation to the masses of these elements in the coatings,the influence of both elements is considerably higher than the influence of zinc alone.For this reason,a new quantity is introduced,called ＂equivalent Zn coating weight＂.This quantity is a linear combination of the coating weights of zinc,aluminium and magnesium.A model for prediction of corrosion resistance was developed with the expert system,based on a combination of regression analysis and a ＂decision tree＂ algorithm.The model was able to correctly classify 25 out of 27 materials based on just the three analytical parameters mentioned above： the total coating weights of zinc,aluminium and magnesium.In conclusion,the approach shows that an accurate prediction of the corrosion behaviour is possible even on-line.For purposes of material development,the expert system can also be expanded to include additional analytical parameters.