Raed Al Kontar, U-M Industrial and Operations Engineering (IOE) assistant professor, has received a National Science Foundation (NSF) grant from the Cyber-physical Systems (CPS) program for a project that mitigates uncertainties in remote computer numerical control (CNC) using data-driven transfer learning.
CNC is a critical feature of modern manufacturing machines. It provides automated control based on a set of programmed instructions, which traditionally run on a local computer that is physically tethered to a machine.
Al Kontar’s work will allow for manufacturing machines to be controlled remotely over the internet through CNC installed on cloud computers.
Among several benefits over traditional CNC, cloud-based CNC holds promise to significantly improve the speed and accuracy of manufacturing machines at low cost. However, a major challenge with cloud-based CNC is that, like video streaming, cloud-based CNC controls manufacturing machines primarily using pre-calculated commands that must be buffered to mitigate internet transmission delays.
For this reason, cloud-based CNC is susceptible to anomalies that result from delayed transmission of information on how the controlled machine is actually behaving. This project aims to predict such anomalies and mitigate them through cloud-based CNC architecture by switching control authority from a cloud controller to a back-up local controller in the event of an impending failure.
“A data-driven transfer learning framework will predict and minimize uncertainties using data obtained from other machines connected to cloud-based CNC,” said Al Kontar.
“What excites me most about this project is the synergy between theory and application. We will work on developing scalable transfer learning methodologies and test and refine these methods based on an actual prototype consisting of a 3D printer controlled from the cloud.”
Al Kontar joined U-M IOE in 2018. His research interests primarily include developing data analytics and decision-making methodologies specifically tailored for the Internet of Things (IoT) enabled smart and connected systems. He also leads the U-M Data Analytics for IoT Enabled Systems Lab.