STTR Phase II: Development of Autocalibration Techniques to Enable Commercial Scale-up of Software-based Vibration Compensation for 3D Printers

STTR 第二阶段：开发自动校准技术，以实现 3D 打印机基于软件的振动补偿的商业规模化

• 批准号: 2233481
• 负责人: Brenda Jones
• 金额: $ 99.97万
• 依托单位: S2A TECHNOLOGIES LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233481&HistoricalAwards=false
• 关键词: STTR; Phase; II; Development; Autocalibration

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.

该小型企业技术转让 (STTR) 第二阶段项目的更广泛/商业影响是通过软件改进以低成本提高制造机器的生产率，而不牺牲质量。该项目的具体动机是 3D 打印，这是一个价值 110 亿美元、快速增长的先进制造业行业，对国家安全、供应链弹性和经济繁荣至关重要。 3D打印在主流制造中的采用常常受到3D打印机速度低的阻碍。高速3D打印的一个主要障碍是振动，这会导致高速运行时的质量损失。该项目旨在开发一种低成本、基于软件的方法来减轻 3D 打印机的振动。所提出的软件解决方案有效性的一个主要障碍是需要精确校准 3D 打印机在不同操作条件下不断变化的振动行为。该项目将开发一套自动校准技术来解决这一障碍。通过该项目开发的软件算法不仅有利于 3D 打印，还适用于各种先进制造机器，例如机床和机器人，其速度和精度通常受到振动的限制。该小型企业技术转让第二阶段项目旨在开发一套自动校准技术，使专有的振动补偿算法能够适应现场 3D 打印机振动行为的频繁变化。为了实现这一目标，将追求三个技术目标。第一个技术目标将涉及开发无传感器自动校准技术，该技术不断更新 3D 打印机的校准，随着材料沉积在打印床上，3D 打印机的移动质量和振动行为会发生变化。第二个技术目标将侧重于开发基于传感器的自动校准技术，该技术使用浅层机器学习来不断微调带有板载加速度计的 3D 打印机的校准图。第三个技术目标将涉及开发基于传感器的自动校准即服务技术，主要用于没有板载加速度计的 3D 打印机。为了实现这些目标，需要进行研究来克服阻碍所提出的自动校准方法的准确性和计算效率的技术障碍。该项目的智力价值在于开发基于科学的方法，克服技术障碍及其相关风险。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。