STTR Phase I: Minimizing Uncertainties in Software-based Vibration Compensation of 3D Printers to Enable Increased Speed and Accuracy (COVID-19)

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The broader impact of this Small Business Technology Transfer (STTR) Phase I project is to increase the productivity (speed) of manufacturing machines at low cost without sacrificing quality. The project is specifically motivated by 3D printing (or additive manufacturing), a $9 billion rapidly growing industry whose adoption for mainstream manufacturing is hindered by the low speed of 3D printers. For example, faster 3D printers can help support manufacturing of key equipment, such as personal protective equipment (PPE). A major hindrance to high-speed 3D printing is vibration, which causes loss of quality at high-speed operation. This project seeks to develop a new approach for mitigating the vibration of 3D printers and other manufacturing machines. Because the dynamic behavior of manufacturing equipment may lead to uncertainties in software compensation schemes, this project will develop new software algorithms to address these uncertainties. The software algorithms developed through this project will not only benefit 3D printing, but would also apply a wide range of manufacturing machines, like machine tools and robots, whose speed and accuracy are often limited by vibration.This STTR Phase I project seek to develop two new calibration approaches that allow the filtered B spline vibration compensation software to handle uncertainty and avoid loss of accuracy due to dynamic mismatch. The first approach is robust offline calibration – i.e., calibration of the machine offline to accommodate the widest range of potential mismatch in machine dynamics. Preliminary lab-scale work has shown potential of a robust filtered basis functions to address this issue. However, remaining technical challenges of guaranteed computational efficiency and accuracy of the robust filtered basis function approach must be overcome. The second approach is adaptive online calibration – i.e., updating the calibration of the machine while it is operating in the field using vibration measurements obtained from low-cost accelerometers. To achieve this, this project will address challenges of guaranteed accuracy of adaptive online calibration using low-cost accelerometers by ensuring persistence of excitation during online calibration.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.