Tackling Motion-Command-Induced Nonlinear Vibration in Manufacturing Machines Using Software Compensation

Quality, productivity and cost are three key pillars of manufacturing. To stay competitive in an increasingly global economy, U.S. manufacturers must find ways to improve the quality and productivity of their manufacturing processes while keeping costs low. 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 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. However, existing software solutions cannot adequately handle nonlinear vibrations which are prevalent in manufacturing machines like 5-axis machine tools, delta 3D printers, and robots.

This award supports a scientific investigation into a software-based vibration mitigation approach that shows great promise to overcome the technical shortcomings of existing software solutions. Knowledge created through this investigation will enable industry to boost the speed and accuracy of manufacturing machines at low cost, thus increasing their competitiveness in the global marketplace.

The objective of this project is to mathematically and experimentally characterize the effectiveness of optimal filtered basis functions, formulated using physics-based and data-driven linear models of nonlinearity, as a means to mitigate motion-command-induced nonlinear vibration in manufacturing machines. Three classes of nonlinearities found in manufacturing machines will be addressed.

Structured nonlinearities, where nonlinear dynamics are described by first-principle equations will be tackled by designing optimal basis functions that utilize the known structure of the nonlinearities for vibration compensation. Unstructured nonlinearities, where nonlinear dynamics are described as uncertainties will be addressed by designing basis functions that are optimized for robust vibration compensation.

Lastly, unknown or unmodeled nonlinearities will be addressed using a data-driven approach where vibration measured online from manufacturing machines will be used to build machine learning models to compensate vibration. The theoretical understanding and methods developed through this research will be validated experimentally on various vibration-prone manufacturing machines with nonlinearities, including a precision motion stage, a delta 3D printer, and a collaborative robot.

Broader impacts of this project will be realized by: (i) cooperating with U.S.-based companies to translate the new methods to industry; (ii) educating industry and the broader public about software based vibration mitigation methods through tutorials and open-access publications; and (iii) K-12 outreach to motivate underrepresented minority students to enter STEM fields by demonstrating the benefits of software-based vibration mitigation techniques on desktop 3D printers.

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.