Machine learning aided accelerated design and characterization of automotive composites

The proposed research project involves developing machine learning models to predict the mechanical properties of polymer composites. The interns will collect and preprocess data from various sources including open-source databases and conducting extensive experimental tests, build artificial neural network (ANN) models using advanced algorithms, and validate the accuracy of these models using test data. The expected benefit to the partner organization, Magna Closures, is the development of accurate and reliable models that can predict the behavior of polymer composites under different conditions, such as tensile tests and creep tests. These models can be used by the partner organization to optimize material selection and design, reduce testing costs, and improve the overall performance of polymer composites in various applications in automotive applications.

Faculty Supervisor:

Reza Rizvi

Student:

Partner:

Magna

Discipline:

Engineering

Sector:

Manufacturing; Wholesale trade

University:

York University

Program: