A Framework for Predictive Modeling of Alloy Corrosion Resistance in Marine Environments

Thesis Abstract

The durability and integrity of metallic alloys used in marine environments pose significant challenges due to the aggressive nature of saline conditions, necessitating advanced predictive frameworks to assess and enhance corrosion resistance. This study aims to develop a comprehensive, data-driven predictive modeling framework that accurately estimates alloy corrosion resistance in marine settings, thereby facilitating informed material selection and maintenance strategies. Specific objectives include identifying key metallurgical and environmental variables affecting corrosion, formulating an integrated modeling architecture combining theoretical insights and empirical data, and validating the framework through experimental corrosion tests. The research adopts a mixed-methods approach, with a quantitative research design grounded in experimental corrosion testing complemented by qualitative analysis of material properties. The target population comprises stainless steel, aluminum alloy, and titanium alloy samples sourced from diversified marine environments, with a total sample size of 150 specimens stratified across alloy types and environmental conditions. Data collection involves laboratory-based electrochemical impedance spectroscopy (EIS), potentiodynamic polarization, surface morphology analysis through scanning electron microscopy (SEM), and environmental monitoring of parameters such as temperature, salinity, and pH levels. The reliability and validity of instruments are assured via calibration procedures, standard reference materials, and repeatability tests, while data analysis employs multiple regression analysis, machine learning algorithms—including support vector machines (SVM)—and analysis of variance (ANOVA) to evaluate the influence of variables on corrosion outcomes. The conceptual framework integrates Stefan’s Electrochemical Corrosion Theory and the Galvanic Corrosion Theory to underpin the predictive models, which are further refined using a combination of statistical and computational techniques to optimize accuracy and robustness. Expected findings indicate that material composition, environmental conditions, and surface treatment significantly influence corrosion resistance and that the integrated modeling framework can predict corrosion behavior with high precision across various alloy-environment combinations. The study also anticipates identifying critical thresholds in environmental parameters that accelerate corrosion processes, thereby offering practical guidance for material selection. The contribution to existing knowledge lies in the development of a unified, adaptable predictive framework that integrates metallurgical and environmental variables using both traditional statistical models and modern machine learning techniques, representing an advancement over existing isolated or purely empirical models. This framework provides a scalable tool for engineers and researchers to forecast alloy performance in marine environments, optimizing maintenance schedules and prolonging service life. The main conclusion highlights the efficacy of combining theoretical corrosion models with empirical data analysis in creating reliable predictive tools. Recommendations include adopting the framework in industrial corrosion management practices, extending research to include bio-corrosion factors, and developing real-time monitoring systems to enhance predictive accuracy. Future research directions suggest integrating sensor data for dynamic model updates and exploring novel alloy compositions for improved marine corrosion resistance, further contributing to sustainable maritime infrastructure development.