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A Bayesian network approach that integrates variables concerning materials composition, processing, and environmental parameters into a single model.
The localized corrosion and cracking of lightweight alloys is a complex, non-linear and stochastic function of the variables concerning materials composition, thermal and mechanical processing, and environmental parameters such as solution chemistry, temperature, electrochemical potential and mechanical stress. Integrating these variables into a coherent model poses a ‘grand challenge’ in corrosion science and engineering. In this conference paper, a Bayesian network approach that integrates these variables into a single model is presented based upon pre-existing models taken from the literature as well as data-sets that provide electrochemical ‘fingerprints’ for the cathodic and anodic behavior of intermetallic particles. Laboratory analyses of the microstructure of 7075 and 2070 alloys and the electrochemical properties of the intermetallic properties provide the inputs for the Bayesian network model. Corrosion fatigue experiments combined with a literature survey to determine statistically distributed crack growth rates are used to generate Paris laws that are incorporated into the model for determination of the pit-to-crack transitions and estimate the overall number of cycles to failure.
Specimens of duplex stainless steel (DSS) were exposed at temperature of 200°C in industrial white liquor from a Brazilian kraft mill using an autoclave to simulate the same conditions of a digester processing.
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The paper describes the development of the methodology for the full ring, full thickness 4-point bend (4PB) and large scale DCB specimens and the results obtained from these tests.
This paper explores the use of remote monitoring systems and web-based data analysis to track corrosion rates in real time.