SBIR-STTR Award

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.

The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase II project is to increase the productivity of manufacturing machines at low cost through software improvements, without sacrificing quality. The project is specifically motivated by 3D printing, an $11 billion and rapidly growing industry within advanced manufacturing that is critical to national security, supply chain resiliency, and economic prosperity. The adoption of 3D printing for mainstream manufacturing is often hindered by the low speed of 3D printers. 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 low-cost, software-based approach for mitigating the vibration of 3D printers. A major impediment to the effectiveness of the proposed software solution is the need for accurate calibration of the changing vibration behavior of 3D printers under varying operating conditions. This project will develop a set of automatic calibration techniques to address this impediment. The software algorithms developed through this project will not only benefit 3D printing but would also apply to a wide range of advanced manufacturing machines, like machine tools and robots, whose speed and accuracy are often limited by vibration. This Small Business Technology Transfer Phase II project seeks to develop a suite of automatic calibration techniques to enable a proprietary vibration compensation algorithm to adapt to frequent changes in vibration behavior of 3D printers that occur in the field. To achieve this goal, three technical objectives will be pursued. The first technical objective will involve the development of a sensor-less auto calibration technique that continuously updates the calibration of 3D printers whose moving mass and vibration behavior changes as material is being deposited on the print bed. The second technical objective will focus on the development of a sensor-based, autocalibration technique that uses shallow machine learning to continuously fine-tune the calibration maps of 3D printers with onboard accelerometers. The third technical objective will involve the development of a sensor-based autocalibration-as-a-service technique primarily for 3D printers that do not have onboard accelerometers. To achieve these objectives, research is needed to overcome technical hurdles that hinder the accuracy and computational efficiency of the proposed auto-calibration approaches. The intellectual merit of this project is in developing science-based approaches that overcome the technical hurdles and their associated risks.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.