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51318-11228-Predicting Corrosion Fatigue Behavior using Bayesian Networks

Picture for Predicting Corrosion Fatigue Behavior using Bayesian Networks
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A Bayesian network approach that integrates variables concerning materials composition, processing, and environmental parameters into a single model.

Product Number: 51318-11228-SG
Author: Jacquelynn Garofano / Kenneth Smith
Publication Date: 2018
Industry: Corrosion Monitoring and Control
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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.

 

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.

 

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