“Improving Flexural Strength Predictions in Composite Materials using Image Processing and Machine Learning”

This project aims to improve the way we assess the strength of short fiber reinforced composites, focusing on sustainability. By exploiting the distinct visibility traits of PEEK and carbon fibers in CT scans, the study will utilize non-destructive testing and computer algorithms to analyze and measure factors critical to the material’s strength directly from scan images. Through a combination of 2D and 3D imaging techniques and machine learning, the research intends to streamline the analysis of large data sets and develop a predictive model linking scan images to the composite’s mechanical properties.
The collaboration between York University and Prof. Gupta at New York University aims to leverage intern’s skills in micro-CT scanning and image processing to advance research in composite materials. The joint work will not only contribute to the lab’s research objectives but also enhance the intern’s understanding of machine learning and the relationship between imaging techniques and material properties. With the intern’s expertise in material charactersation, CT scanning and image processing, this partnership is expected to yield innovations in sustainable material testing and development, benefiting both institutions by fostering academic growth and opening new research avenues in materials science.

Faculty Supervisor:

Reza Rizvi;Garrett Melenka

Student:

Partner:

New York University Polytechnic School of Engineering

Discipline:

Engineering

Sector:

Artificial Intelligence; Technology; Advanced Manufacturing

University:

York University

Program: