CAREER: Adding to the Future: Thermal Modeling, Sparse Sensing, and Integrated Controls for Precise and Reliable Powder Bed Fusion

该学院早期职业发展计划(Career)项目将在添加剂制造(AM)过程中实现更高的精确度和更大的重复性。与传统加工不同的是，零件是通过切割掉不需要的材料来制造的，而添加制造--也被称为3D打印--通过逐步添加少量材料来制造出前所未有的复杂的三维物体。粉末床熔融(PBF)是一种常用的粉末熔融技术，它是一种制造复杂金属或高性能聚合物零件的AM技术，它是通过施加和选择性熔化粉末原料来制造新材料的过程。该项目支持基础研究，以创建新的热建模、传感和控制算法，从而实现精确可靠的PBF。该建模任务将能够快速准确地预测粉末熔化过程中的热流和温度分布。由此产生的关于引导热流的知识对于获得所需的三维形状是必不可少的。传感任务将制定新的信号处理算法，丢弃不必要的信息，以充分利用高速视频等数据密集型传感器来源。最后，这些结果将与新的控制算法相结合，以抵消工艺变化，并提供可重复、低成本、高质量的部件。AM在能源、航空航天、汽车、医疗保健和生物医药行业的广泛产品中提供了尚未开发的潜力。在从先进的喷气发动机部件到定制设计的医疗植入物的各种应用中，PBF部件越来越受到青睐。因此，该项目的成果将有助于制造有利于美国经济和提高生活质量的产品。通过24所合作大学的网络传播教育成果，将扩大该项目的更广泛影响，将创新解决问题的技能灌输到本科工程教育中。粉末床熔合工艺利用精密加热和快速凝固，以及对原料应用和激光或电子束的扫描速度和路径进行逐层调整。该项目将在建模和过程控制的界面上扩展知识，以考虑与AM进行精密制造的主要障碍具体地说，该项目将解决(1)缺乏捕捉多尺度热机械相互作用的易处理的在线模型的问题，以及(2)在存在有限带宽传感器反馈的情况下对控制策略的需求。通过分离交叉扫描和跨层动力学，将产生一个动态实时模型，允许使用计算友好的原语实时处理当前难以处理的粉末熔化动力学。然后，将使用过程动力学的结构来实现激光能量沉积的反馈控制器。控制适当的能量沉积是确保质量和重复性的关键。该方法将基于最初在精密机电一体化领域开发的建模和自适应方法，并结合能够以快速、用户可配置的采样率抑制结构化热扰动的多速率控制公式。总体而言，该项目将通过(1)建立基于物理的、面向控制的建模方法来理解和设计分层热相互作用，并通过(2)为闭环系统控制解决方案创建基础，以在周期性和近周期的热能沉积中产生所需的均匀温度场，从而为未来重复的和逐层的热机械过程增加所需的质量保证方面的新知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development Program (CAREER) project will enable substantially higher accuracy and greater reproducibility in additive manufacturing (AM) processes. In contrast to conventional machining, where parts are made by cutting away unwanted material, additive manufacturing -- also called 3D printing -- builds three-dimensional objects of unprecedented complexity by progressively adding small amounts of material. Powder bed fusion (PBF), in which new material is added to the part being fabricated by applying and selectively melting a powdered feedstock, is a popular form of AM for fabricating complex metallic or high-performance polymeric parts. This project supports fundamental research to create new thermal modeling, sensing, and control algorithms that will lead to precise and reliable PBF. The modeling task will enable fast and accurate prediction of heat flow and temperature distribution during powder fusion. The resulting knowledge on directing heat flow is essential for achieving a desired three-dimensional shape. The sensing task will formulate new signal processing algorithms that discard unnecessary information to make full use of data-intensive sensor sources like high-speed video. Finally, these results will be integrated with new control algorithms in order to counteract process variations and provide repeatable, low-cost, high-quality parts. AM offers untapped potential in a wide range of products for the energy, aerospace, automotive, healthcare, and biomedical industries. PBF parts are increasingly preferred in applications ranging from advanced jet-engine components to custom-designed medical implants. Therefore, the outcomes of this project will facilitate fabrication of products to benefit the US economy and improve quality of life. Broader impacts of the project will be augmented by dissemination of educational results via a network of twenty-four collaborating universities, to inculcate skills for innovative problem solving into undergraduate engineering education. The powder bed fusion process exploits precision heating and rapid solidification, together with layer-by-layer adjustments to feedstock application, and scan speed and path of lasers or electron beams. This project will expand knowledge at the interface of modeling and process controls, to consider the main obstacles to precision manufacturing with AM. Specifically, the project will address (1) the lack of tractable online models that capture multi-scale thermomechanical interactions, and (2) the need for control strategies in the presence of limited-bandwidth sensor feedback. A dynamic real-time model will be produced through separation of the cross-scan and cross-layer dynamics, allowing currently intractable powder fusion dynamics to be treated in real time, using computation-friendly primitives. Then the structure of the process dynamics will be used to enable a feedback controller for laser energy deposition. Controlling the proper energy deposition is critical for ensuring quality and reproducibility. The approach will be based on modeling and adaptation methods originally developed in precision mechatronics, in conjunction with a formulation of multi-rate control that can reject structured thermal disturbances at a fast, user-configurable sampling rate. Collectively, the project will add the needed new knowledge on quality assurance to future repetitive and layer-by-layer thermomechanical processes, by (1) establishing a physics-based, control-oriented modeling approach to understand and engineer the layered thermal interactions, and by (2) creating a foundation for closed-loop control solutions to produce desired uniform temperature fields in periodic and near-periodic deposition of thermal energy.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.