Fandi Meng, Yufan Chen, Jianning Chi, Huan Wang, Fuhui Wang, Li Liu
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引用次数: 0
Abstract
The rapid failure of organic coatings in deep-sea environments complicates accurate lifetime prediction. Given the rapid cracking characteristic on the coating surface in this environment, a comprehensive “performance-structure” failure model was established. Initially, a targeted image recognition approach containing convolutional neural network (CNN) and post-processing was constructed for the crack area detection. An overall precision of 82.81% demonstrated the network’s good accuracy. The length distribution and the statistical evolution of cracks were extracted from SEM images to obtain the kinetic equation of the cracks related to coating structure degradation. In addition, the kinetics of water diffusion and coating adhesion were examined, as they represent critical parameters of coating performance. Based on this achievement, a failure model incorporating three dominant factors was integrated by the gray relational analysis method. The average prediction error of the model was 2.60%, which lays the groundwork for developing image-based methods to predict coating life.
期刊介绍:
npj Materials Degradation considers basic and applied research that explores all aspects of the degradation of metallic and non-metallic materials. The journal broadly defines ‘materials degradation’ as a reduction in the ability of a material to perform its task in-service as a result of environmental exposure.
The journal covers a broad range of topics including but not limited to:
-Degradation of metals, glasses, minerals, polymers, ceramics, cements and composites in natural and engineered environments, as a result of various stimuli
-Computational and experimental studies of degradation mechanisms and kinetics
-Characterization of degradation by traditional and emerging techniques
-New approaches and technologies for enhancing resistance to degradation
-Inspection and monitoring techniques for materials in-service, such as sensing technologies