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

职业：为未来添砖加瓦：热建模、稀疏传感和集成控制，实现精确可靠的粉床融合

• 批准号: 1750027
• 负责人: Xu Chen
• 金额: $ 50万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750027&HistoricalAwards=false
• 关键词: CAREER; Adding; Future; Thermal; Modeling

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.

该教师早期职业发展计划（CAREER）项目将在增材制造（AM）过程中实现更高的准确性和更高的再现性。与传统的机械加工相比，零件是通过切除不需要的材料制成的，增材制造-也称为3D打印-通过逐步添加少量材料来构建前所未有的复杂三维物体。粉末床熔融（PBF）是一种用于制造复杂金属或高性能聚合物部件的AM的流行形式，其中通过施加和选择性熔化粉末原料将新材料添加到正在制造的部件中。该项目支持基础研究，以创建新的热建模，传感和控制算法，这将导致精确和可靠的PBF。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。由此产生的引导热流的知识对于实现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，并提供可重复的，低成本的，高质量的零件。AM在能源、航空航天、汽车、医疗保健和生物医学行业的广泛产品中提供了尚未开发的潜力。PBF零件在从先进的喷气发动机部件到定制设计的医疗植入物等应用中越来越受到青睐。因此，该项目的成果将促进产品的制造，以造福美国经济和提高生活质量。该项目的更广泛的影响将通过24所合作大学的网络传播教育成果来扩大，以向本科工程教育灌输创新解决问题的技能。粉末床熔融工艺利用精确加热和快速固化，以及逐层调整原料应用，以及激光或电子束的扫描速度和路径。本项目将扩大知识的接口建模和过程控制，考虑的主要障碍，精密制造与增材制造。具体而言，该项目将解决（1）缺乏捕获多尺度热机械相互作用的易处理的在线模型，以及（2）在有限带宽传感器反馈的情况下需要控制策略。将通过分离交叉扫描和跨层动态产生动态实时模型，从而使用计算友好的基元真实的实时处理当前棘手的粉末熔化动态。然后，该结构的过程动力学将被用来使激光能量沉积的反馈控制器。控制适当的能量沉积对于确保质量和再现性至关重要。该方法将基于最初在精密机电一体化中开发的建模和自适应方法，结合多速率控制的制定，可以以快速，用户可配置的采样速率拒绝结构化热干扰。总的来说，该项目将通过以下方式为未来的重复和逐层热机械过程添加所需的质量保证新知识：（1）建立基于物理的、面向控制的建模方法，以理解和设计分层热相互作用，以及通过（2）为闭环控制解决方案创建基础，以在周期性和近周期性中产生期望的均匀温度场，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。

项目成果

Synthesis and Analysis of Multirate Repetitive Control for Fractional-order Periodic Disturbance Rejection in Powder Bed Fusion

粉床熔融分数阶周期抗扰多速率重复控制的综合与分析

• DOI: 10.11509/isfa.2018.30
• 发表时间: 2018
• 期刊: Proceedings of 2018 International Symposium on Flexible Automation
• 作者: Wang, Dan;Chen, Xu
• 通讯作者: Chen, Xu

Realtime Control-oriented Modeling and Disturbance Parameterization for Smart and Reliable Powder Bed Fusion Additive Manufacturing

面向实时控制的建模和干扰参数化，实现智能可靠的粉床熔融增材制造

• 发表时间: 2018
• 期刊: Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference
• 作者: Chen, Xu;Jiang, Tianyu;Wang, Dan;Xiao, Hui
• 通讯作者: Xiao, Hui

Rejecting fast narrow-band disturbances with slow sensor feedback for quality beam steering in selective laser sintering

通过慢速传感器反馈抑制快速窄带干扰，以实现选择性激光烧结中的高质量光束控制

• DOI: 10.1016/j.mechatronics.2018.09.002
• 发表时间: 2018
• 期刊: Mechatronics
• 影响因子: 3.3
• 作者: Xiao, Hui;Jiang, Tianyu;Chen, Xu
• 通讯作者: Chen, Xu

A Spectral Analysis of Feedback Regulation Near and Beyond Nyquist Frequency

• DOI: 10.1109/tmech.2018.2795960
• 发表时间: 2018-01
• 期刊: IEEE/ASME Transactions on Mechatronics
• 作者: Dan Wang;Xu Chen

A multirate fractional-order repetitive control for laser-based additive manufacturing

• DOI: 10.1016/j.conengprac.2018.05.001
• 发表时间: 2018-08
• 期刊: Control Engineering Practice
• 影响因子: 4.9
• 作者: Dan Wang;Xu Chen