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: 1953155
• 负责人: Xu Chen
• 金额: $ 43.09万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-16 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953155&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.

该学院早期职业发展计划（Career）项目将大大提高增材制造（AM）工艺的准确性和可重复性。传统机械加工通过切割不需要的材料来制造零件，而增材制造（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）是一种流行的增材制造形式，用于制造复杂的金属或高性能聚合物部件，通过应用和选择性熔融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，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合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实现所期望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，充分利用高速视频等数据密集型传感器资源。最后，这些结果将与新的控制算法集成，以抵消工艺变化，并提供可重复，低成本，高质量的零件。增材制造在能源、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。从先进的喷气发动机部件到定制设计的医疗植入物，PBF部件越来越受到人们的青睐。因此，该项目的成果将促进产品的制造，有利于美国经济，提高生活质量。通过24所合作大学的网络传播教育成果，将扩大项目的广泛影响，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凝固，以及对原料应用的逐层调整，以及激光或电子束的扫描速度和路径。该项目将扩展建模和过程控制界面的知识，以考虑增材制造精密制造的主要障碍。具体而言，该项目将解决(1)缺乏捕获多尺度热-机械相互作用的可处理在线模型，以及(2)在有限带宽传感器反馈存在下需要控制策略。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，使用计算友好的原语，可以实时处理当前棘手的粉末融合动力学。然后利用过程动力学结构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现性的关键。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，结合多速率控制的公式，可以以快速，用户可配置的采样率拒绝结构化热干扰。总的来说，该项目将通过(1)建立一种基于物理的、面向控制的建模方法来理解和设计分层热相互作用，以及(2)为闭环控制解决方案奠定基础，以在周期性和近周期性的热能沉积中产生所需的均匀温度场。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。

项目成果

Control-Oriented In Situ Imaging and Data Analytics for Coaxial Monitoring of Powder Bed Fusion Additive Manufacturing

用于粉末床熔融增材制造同轴监控的面向控制的原位成像和数据分析

• DOI: 10.1520/stp163720200104
• 发表时间: 2022
• 期刊: PROGRESS IN ADDITIVE MANUFACTURING 2020
• 作者: Tianyu Jiang;Mengying Leng;Xu Chen
• 通讯作者: Xu Chen

Preheating Temperature Control and Low-Contrast Imaging Data Analytics for Laser Powder Bed Fusion

激光粉末床融合的预热温度控制和低对比度成像数据分析

• DOI: 10.1109/tmech.2022.3202573
• 发表时间: 2023
• 期刊: IEEE/ASME Transactions on Mechatronics
• 作者: Jiang, Tianyu;Leng, Mengying;Chen, Xu
• 通讯作者: Chen, Xu

Control-Oriented Modeling and Repetitive Control in In-Layer and Cross-Layer Thermal Interactions in Selective Laser Sintering

选择性激光烧结中层内和跨层热相互作用的面向控制的建模和重复控制

• DOI: 10.1115/1.4046367
• 发表时间: 2021
• 期刊: ASME Letters in Dynamic Systems and Control
• 作者: Wang, Dan;Jiang, Tianyu;Chen, Xu
• 通讯作者: Chen, Xu

New Hammerstein Modeling and Analysis for Controlling Melt Pool Width in Powder Bed Fusion Additive Manufacturing

• DOI: 10.1115/1.4050079
• 发表时间: 2021-07
• 作者: Dan Wang;Xinyu Zhao;Xu Chen

A combined theoretical and experimental approach to model polyamide 12 degradation in selective laser sintering additive manufacturing

选择性激光烧结增材制造中聚酰胺 12 降解模型的理论与实验相结合的方法

• DOI: 10.1016/j.jmapro.2021.08.051
• 发表时间: 2021
• 期刊: Journal of Manufacturing Processes
• 影响因子: 6.2
• 作者: Yang, Feifei;Chen, Xu
• 通讯作者: Chen, Xu