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Meeting MS&T21: Materials Science & Technology
Symposium Computation Assisted Materials Development for Improved Corrosion Resistance
Presentation Title Development of a Damage Function for Galvanic Corrosion Degradation of Coated Al Alloy Systems
Author(s) Mahdi Jokar, Gerald Frankel
On-Site Speaker (Planned) Mahdi Jokar
Abstract Scope Artificial Neural Networks (ANNs) and random forest regression were used to develop a predictive damage function for galvanic corrosion of 7075-T6 Al alloy panel with different coating systems in various environmental factors. In this research, lost volume has been modeled based on different parameters such as pretreatment, primer coating, topcoat, chloride concentration, RH, galvanic current, impressed current and environment. The best model was based on lost volume as output and the same factors except environment descriptors as inputs. The room mean square error (RMSE) for this function was 0.2 mm3. Although the RMSE was higher than for some other models, it is more realistic not to use standard environments as an input. To predict lost volume in this formula, the ANN model involved three nodes in one hidden layer with hyperbolic tangent functions. The ANN was able to get a good fit for training and validation (RMSE=0.2 and R2=0.7).

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Back to the Basics: Revisiting Copper to Build Thermodynamic Corrosion Models
Computational Modeling of Corrosion and Mechanical Failure in Magnesium-Aluminum Vehicle Joints
Development of a Damage Function for Galvanic Corrosion Degradation of Coated Al Alloy Systems
Factors That Influence Materials Corrosion and How Modeling May Predict These Effects
First Steps Towards a Coupled Thermodynamic-kinetic Model to Predict Sulfate Deposit Induced Hot Corrosion of Aluminized Ni-based Superalloys
Hydrothermal Corrosion of Silicon Carbide
Modeling of High-temperature Corrosion of Zirconium Alloys Using the eXtended Finite Element Method (X-FEM)
Modelling Alkoxide Corrosion Initiation of Pure-aluminum in Ethanol with Integrated Simulation-based Experimental Methods
Modelling Microstructural Evolution of Aluminide Coatings on Ni-based Superalloys
Morphological Stability of Electrostrictive Thin Films
P2-17: Development of Rhenium Free Heat-resistant Nickel Alloy for the Cast Blades Production by the Method of Directional Crystallization
Predictive Modeling of Microstructure Induced Variations in the Sensitization Response of 5XXX Aluminum Alloys
Solubility Based Prediction of Corrosion in Molten Chloride Salts
Understanding and Reducing Bias in Machine Learning to Enhance Its Predictive and Extrapolative Capabilities: Application to the Oxidation Kinetics and Spallation Behavior of High-temperature NiCr-based Alloys

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