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

质量、生产率和成本是制造业的三大支柱。为了在日益全球化的经济中保持竞争力，美国制造商必须设法提高制造过程的质量和生产率，同时保持低成本。由于机械结构的弱点，制造机器在移动时往往会振动。由此产生的运动引起的振动对机器的精度和速度产生不利影响，从而降低了相关制造过程的质量和生产率。软件解决方案包括生成运动命令以避免机器不必要的振动，在实践中非常有吸引力，因为它们成本低。然而，现有的软件解决方案不能充分处理非线性振动，这在制造机器中很普遍，如五轴机床、delta 3D打印机和机器人。该奖项支持对基于软件的振动缓解方法进行科学研究，该方法有望克服现有软件解决方案的技术缺陷。通过这项调查创造的知识将使工业能够以低成本提高制造机器的速度和准确性，从而提高他们在全球市场上的竞争力。该项目的目标是通过数学和实验表征最优过滤基函数的有效性，该函数使用基于物理和数据驱动的非线性线性模型来制定，作为减轻制造机器中运动命令引起的非线性振动的一种手段。在制造机器中发现的三类非线性将被处理。结构非线性，其中非线性动力学是由第一性原理方程描述的，将通过设计利用非线性的已知结构进行振动补偿的最优基函数来解决。非结构化非线性，其中非线性动力学被描述为不确定性，将通过设计优化的鲁棒振动补偿基函数来解决。最后，将使用数据驱动的方法解决未知或未建模的非线性问题，其中从制造机器在线测量的振动将用于构建机器学习模型来补偿振动。通过本研究开发的理论理解和方法将在各种具有非线性的易振动制造机器上进行实验验证，包括精密运动平台，delta 3D打印机和协作机器人。该项目将通过以下方式实现更广泛的影响：(i)与美国公司合作，将新方法转化为工业；（ii）通过教程和开放获取出版物，向工业界和广大公众宣传基于软件的减振方法；（iii） K-12外展，通过展示桌面3D打印机上基于软件的振动缓解技术的好处，激励代表性不足的少数民族学生进入STEM领域。该奖项反映了美国国家科学基金会的法定使命，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。

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.