CPS: Small: Mitigating Uncertainties in Computer Numerical Control (CNC) as a Cloud Service using Data-Driven Transfer Learning

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Computer numerical control (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 the machine. This work envisions a future where manufacturing machines are controlled remotely over the Internet using 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, somewhat like video streaming, it 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. The award supports a scientific investigation into approaches for predicting impending anomalies from data gathered from past experience, and using the predictions to avoid incorrect control actions resulting from inadequate feedback. The U.S. stands to benefit economically from a transition from traditional to cloud-based CNC, since the U.S. is by far the market leader in cloud-based services. The project also will include outreach to U.S. companies, educational curriculum development to increase the U.S. talent pool in manufacturing and data analytics, and activities for middle schoolers in the Detroit area to inspire them to pursue careers in engineering.The objective of the project is to mitigate uncertainties associated with real-time control of manufacturing machines from the cloud using data-driven transfer learning. The knowledge gained will boost the performance of manufacturing machines at low cost by providing the machines with reliable cloud-based CNC. In cloud-based CNC, advanced feedforward control functionalities are transitioned to the cloud while fast feedback loops are retained locally. However, with emphasis on feedforward control, uncertainties in modeling the dynamic behavior of machines could degrade the reliability and performance of cloud-based CNC by causing failures, due to inaccurate control actions. The system will predict failures using measured signals and mitigate them in a redundant, 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. To this end, a data-driven transfer learning framework will predict and minimize uncertainties using data obtained from other machines connected to cloud-based CNC. Such transfer learning leverages data from one source to learn a different, but related, target source. The framework will allow cloud-based CNC to: (i) learn from a combination of condition monitoring signals and past failure data to predict impending failures, (ii) reduce uncertainties by leveraging condition monitoring data to calibrate physical models whose parameters are functions of their inputs, and (iii) plan feasible trajectories for switching from a cloud to a local controller when an impending failure is detected. The project will address the shortcomings of existing transfer learning methods by: (i) predicting failure events from a combination of condition monitoring and past failure data, and (ii) calibration of physics-based models with functional parameters from condition monitoring data. The methods will be evaluated experimentally on a CPS test bed consisting of a 3D printer controlled from the cloud using a cloud-based CNC prototype.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.