The University of Western Ontario

Published purpose

Robotic based processing is a very attractive option for manufacturers due to the flexibility and re-configurability of the systems as business and market demands change. Within the robotic space, collaborative robots further extend the flexibility of traditional robotized systems by incorporating highly sensitive internal sensors to better perceive the environment around it. These sensors can be used to provide a source of time-series signals representing the process. This project will look to develop a generalized semi-supervised machine learning based predictive framework to estimate the surface roughness of a machine finished component to better improve the efficiency of robotic finishing applications. With an accurate predictor of the surface roughness derived from the processing data provided by the collaborative robot, requirements for additional external inspection can be reduced or removed. A fundamental component of the project is to ensure generalization of the framework, allowing for extendibility into other collaborative robotic manufacturing processes.