CARTE seed projects seminar series is pleased to welcome Katie Xu (a 2^nd year MASc student in the Department of Mechanical and Industrial Engineering, supervised by Professor Chi-Guhn Lee) and JiaHui Zhang (3^rd year PhD candidate student in the Department of Material Science and Engineering, supervised by Professor Yu Zou)

To attend this seminar, please contact CARTE[AT]utoronto.ca

Abstract: Additive manufacturing has important advantages over traditional manufacturing processes. However, as these are relatively new manufacturing technologies, problems of reliability and parameter optimization are not yet fully addressed. We are working towards addressing some of these issues through in-situ monitoring and closed-loop control, using machine learning as a tool. This presentation will cover two parallel streams of research towards this goal. First, is the implementation of in-situ monitoring hardware for a fused deposition modelling (FDM) printer and the construction of dataset for the purpose of building a virtual model of the physical system. Second, is the application of image segmentation approach to expeditiously “deep-mine” the X-ray images captured during the laser powder bed fusion (LPBF) printing process. Our work could provide some basic ideas on how machine learning methods assist in monitoring and controlling the additive manufacturing process.