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

这个小型企业技术转让（STTR）第一阶段项目的更广泛的影响是在不牺牲质量的情况下以低成本提高制造机器的生产率（速度）。该项目的具体动机是3D打印（或增材制造），这是一个价值90亿美元的快速增长的行业，其主流制造业的采用受到3D打印机低速的阻碍。例如，更快的3D打印机可以帮助支持关键设备的制造，例如个人防护设备（PPE）。 高速3D打印的一个主要障碍是振动，这会导致高速操作时的质量损失。该项目旨在开发一种新的方法来减轻3D打印机和其他制造机器的振动。由于制造设备的动态行为可能会导致软件补偿方案的不确定性，因此本项目将开发新的软件算法来解决这些不确定性。通过该项目开发的软件算法不仅有利于3D打印，还将广泛应用于机床和机器人等制造机器，这些机器的速度和精度通常受到振动的限制。该STTR第一阶段项目旨在开发两种新的校准方法，使滤波B样条振动补偿软件能够处理不确定性并避免因动态失配而导致的精度损失。第一种方法是鲁棒的离线校准，即，离线校准机器，以适应机器动态中最大范围的潜在失配。初步的实验室规模的工作已经显示出强大的滤波基函数来解决这个问题的潜力。然而，必须克服鲁棒滤波基函数方法的保证计算效率和准确性的剩余技术挑战。第二种方法是自适应在线校准，即，当机器在现场操作时，使用从低成本加速度计获得的振动测量值来更新机器的校准。为了实现这一目标，该项目将通过确保在线校准期间激励的持续性，解决使用低成本加速度计进行自适应在线校准的保证准确性的挑战。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.