Haeshini Jegan

Advisor: Nian Liu

will defend a Master’s thesis entitled,

REAL-TIME CORROSION MONITORING AND AI-DRIVEN EIS SPECTRA PREDICTION FOR ALUMINIUM BEVERAGE CANS

On

Wednesday, April 16th at 9:30 a.m.
J. Erskine Love Room 295

and

 Virtually via MS Teams

https://teams.microsoft.com/l/meetup-join/19%3ameeting_MWNlMzExYmMtMTdlMi00OGNmLThlNTgtODEwYTYzMmIwNjE1%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2283da4773-90b7-4c7f-a6ff-33f02f623f51%22%7d

Committee
            Prof. Nian Liu – School of ChBE (advisor)
            Prof. Anju Toor – School of MSE (co-advisor)
            Prof. Preet Singh – School of MSE

Abstract

Coatings applied to the interior surfaces of aluminum beverage can-lids play a critical role in preventing corrosion and maintaining the integrity of the product throughout its shelf life. However, these coatings are susceptible to long-term degradation due to mechanical stresses during fabrication and the corrosive nature of various beverages. Traditional multi-month pack tests are laborious and time-consuming, prompting the need for faster, yet accurate, alternatives. This thesis presents an innovative method for evaluating can lid coating degradation by incorporating in-situ Electrochemical Impedance Spectroscopy (EIS) monitoring under real-world conditions using actual beverages. The developed methodology enables accelerated yet insightful coating performance assessments and has the potential to serve as an efficient surrogate to conventional long-duration tests. In parallel, this research explores the integration of machine learning into electrochemical analysis to enhance corrosion prediction capabilities. A Long Short-Term Memory (LSTM) neural network was developed to predict EIS spectra, enabling real-time and data-driven assessment of coating degradation. This study demonstrates the potential of combining advanced electrochemical techniques with machine learning to understand corrosion monitoring. The approach not only improves the evaluation of coated aluminum lids but also offers scalability to broader applications involving aluminum and its alloys in diverse industrial environments.