This project focuses on building machine learning models to predict the corrosion performance of coated steel plates using data such as weight change, capacitance (electrochemical measurements), and adhesion strength. It establishes a framework to scale from simple lab experiments to predictive corrosion modeling.
This project aims to translate the ASTM D8370-22 standard for Field Measurement of Electrochemical Impedance on Coatings and Linings into a portable diagnostic tool for quick, on-site coating evaluation.
This project demonstrates the use of a portable magnetic flux leakage (MFL) setup built on STMicroelectronics’ SensorTile.box platform for detecting sub-coating defects in steel sheets. It shows how artificial gaps and different coatings affect flux leakage signals, validating the feasibility of low-cost embedded NDT.
This project explores a magnetic sensor–based technique for detecting wheel misalignment, focusing on toe and camber angles. Misalignment can affect vehicle performance, safety, and tire wear. Traditional alignment machines are bulky and expensive. This work demonstrates how a compact magnetometer setup can reliably measure misalignments.
This project demonstrates an Arduino-controlled dual peristaltic pump system designed to automate cyclic wetting–drying experiments used in coating and corrosion performance testing.