Machine learning X-ray emission spectra of metal impurities in aluminum alloys

Aluminum alloys are widely used in many industrial applications, including automobile industries, thanks to their outstanding thermal conductivity, lightweight, and low cost. However, the varied range of metal impurities contained in aluminum alloys from different suppliers makes it difficult to control the quality during manufacturing processes. This problem is exacerbated by the lack of a suitable method to characterize the properties of such dilute metallic impurities. The proposed research project will develop a novel method to investigate the properties of metal impurities in aluminum alloys, exploiting the extreme sensitivity of X-ray Spectroscopy on physical properties of dilute impurities. The development of rapid assessment method could make a significant impact on improving aluminum processing in the automobile industry in general.

Faculty Supervisor:

Young-June Kim

Student:

Partner:

Dana Canada Corporation

Discipline:

Physics

Sector:

Manufacturing

University:

University of Toronto

Program: