Parameter Optimization for Additive Manufacturing using Machine Learning

Additive manufacturing (AM), also known as 3D printing, is a process of building products with the material layer by layer. It can produce the products with complex geometries in a simple setup. But it is a challenge in selecting right printing parameters to build a quality part. This project proposes using machine learning techniques for selection of process parameters to improve the AM efficiency and product quality and reduce costs. The expected solution of this project has significant potential for an efficient and sustainable AM processing tool used by manufacturing industries to improve the AM efficiency and product quality while simultaneously reducing printing time and cost.

Faculty Supervisor:

Qingjin Peng

Student:

Partner:

North Forge

Discipline:

Engineering

Sector:

Education; Management of companies and enterprises; Professional, scientific and technical services

University:

University of Manitoba

Program: