Characterization, Modelling and AI-Monitoring of Additive-Subtractive Manufacturing of Biomedical and Aerospace Components

Additive manufacturing (AM) has gained significant interest of aerospace and biomedical industries to produce light weight components with complex geometries. Laser powder bed fusion (LPBF) technology is an AM, process which can improve the productivity and the material efficiency of low volume-high variety manufacturing. After the AM process, the parts usually require a certain level of post-processing subtractive manufacturing (machining) to achieve the desired surface finish and dimensional and geometric accuracies.
The terminal objective of the proposed research is to characterize and optimize the hybrid additive-subtractive manufacturing (ASM) process to improve and control the quality of the manufactured parts for aerospace and bio-medical applications.
In this project, the following three tasks were identified: 1) Optimization of the Hybrid Additive-Subtractive Manufacturing ASM of Aerospace and Biomedical Components; 2) Modelling and Simulation of Additive Manufacturing Process to Minimize Part Distortion; and 3) Artificial Intelligence – Based Process Monitoring and Adaptive Control.

Faculty Supervisor:

Helmi Attia

Student:

Partner:

SECO Tools Canada Inc.

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services; Retail trade

University:

McGill University

Program: