Boosting the Speed and Accuracy of Vibration-Prone Manufacturing Machines at Low Cost through Software

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Quality, productivity and cost are three key pillars of manufacturing. To stay competitive in an increasingly global economy, U.S. manufacturers must find ways of improving the quality and productivity of their manufacturing processes while keeping costs low. Most manufacturing machines tend to vibrate as they move, due to weaknesses in their mechanical structures. The resultant motion-induced vibration adversely affects the accuracy and speed of the manufacturing machines, thus degrading the quality and productivity of the associated manufacturing processes. Software solutions that involve generating motion commands to avoid unwanted vibration of the machines are very attractive in practice because they are low cost and, unlike hardware solutions, they do not add to machine weight and size. However, existing software solutions sacrifice motion speed and/or accuracy, or are impractical because they cannot properly handle uncertainties and variabilities that occur during normal usage of the machines. This award supports a scientific investigation into a software-based vibration mitigation approach that shows great promise to overcome the technical and practical shortcomings of existing software solutions. The approach involves representing the desired machine tools in B-splines, then modifying them to account for characteristics of the machine. To keep calculations manageable, tool motions will not be calculated for the entire part but will be calculated for a "window" around the current tool location. Knowledge created through this scientific investigation will enable industry to boost the accuracy and speed of manufacturing machines at low cost, thus increasing their competitiveness in the global marketplace. This directly affects a number of economic sectors, including medical devices, automotive, aerospace and defense; it therefore directly and positively impacts both economic competitiveness and national security. The broader impact plan includes: educating students and industry about software based vibration mitigation methods through curriculum development and (online) tutorials; and K-12 outreach to motivate underrepresented minority students to STEM fields by demonstrating the benefits of software-based vibration mitigation techniques on desktop 3D printers.The objective of the work is to mathematically characterize and experimentally validate the effects of limited-preview filtering of B-splines on the accuracy and speed of manufacturing machines that suffer from motion-command-induced vibration. The motion commands for a vibration-prone machine will be represented as B-splines. To facilitate computationally efficient online vibration compensation, the B-splines will be filtered in small batches (limited preview) using a model of machine dynamics. However, limited-preview filtering of B-splines introduces approximation errors with poorly understood effects on the accuracy and versatility of online vibration compensation. Methods from linear systems theory will be employed to characterize and mitigate the effects of the approximation errors. Moreover, effects of uncertainties in machine dynamics on the accuracy of filtered B-splines will be analyzed mathematically with a goal of maximizing the robustness of online vibration compensation to variations in system dynamics. Lastly, techniques from model predictive control will be leveraged to develop a scientific methodology for maximizing the speed of vibration-prone machines without sacrificing positioning accuracy. The theoretical understanding and methods developed through this research will be validated experimentally on 3D printers and various other vibration-prone manufacturing machines.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.