Process parameter optimization for metal additive manufacturing: AI modeling

Additive manufacturing (AM) is a product construction process by adding successive layers of materials according to computer-aided design models. Recently, metal AM products have been increasingly applied in aerospace, automotive, and biomedical industries. However, the complex interactions (i.e. thermal field) between metallic components and manufacturing processes make the product qualities inconsistent and unreliable, resulting in expensive product cost. This project aims to construct a model to simulate the thermal field of various metal AM products offline. This model will provide an insight of the whole manufacturing process, and enable constructing relationships between product qualities, key process parameters, and thermal information. With these models, the metal AM process could be monitored, controlled, and optimized effectively, therefore increasing the product quality and reducing the product cost.

Faculty Supervisor:

Gary Wang

Student:

Partner:

University of Tampere

Discipline:

Engineering

Sector:

Advanced Manufacturing; Artificial Intelligence; Other

University:

Simon Fraser University

Program: