A PDE Framework for Sensing and Control of Metal Additive Manufacturing

用于金属增材制造传感和控制的偏微分方程框架

• 批准号: 2222250
• 负责人: Mamadou Diagne
• 金额: $ 59.81万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222250&HistoricalAwards=false
• 关键词: PDE; Framework; Sensing; Control; Metal

This award will allow the development of new analytical frameworks to improve the control of metal additive manufacturing processes. Additive manufacturing technologies have revolutionized design and manufacturing, allowing rapid customization of production and product development by creating three-dimensional objects from digital models. Despite substantial efforts over the past two decades, manufacturing flaws such as geometric inaccuracy, cracks, and micro-structural inhomogeneity often occur during the build. These issues primarily arise from a variety of factors, including the interaction between the laser beam and powder bed, that affect the thermal history during the manufacturing process. This research aims to improve the manufacturing of functional metallic final products through theoretical and experimental investigations that will enable better regulation and control of the temperature cooling rate and the width of the melt-pool, both are key factors in printing complex geometries. The successful completion of this project will positively impact the use of metal additive manufacturing for fabrication of functional parts in high-value industries such as aerospace and defense. Through a thorough outreach and education plan, undergraduate and graduate students will participate in interdisciplinary learning activities that couple advanced manufacturing with system theory. Existing control approaches for metal additive manufacturing processes such as selective laser melting are built on approximate ordinary differential equation (ODE) models or lumped parameter assumptions. These simplified approaches do not precisely capture the spatially distributed nature of the temperature field and completely ignore phase change dynamics, the effect of layer geometry, as well as prior laser passes. Conversely, predictive partial differential equation (PDE) models with phase change dynamics are too complex for real-time control. Through this research effort, we offer control-oriented PDE-ODE models derived from fundamental conservation laws that incorporate phase-change dynamics and enable real-time estimation and control of process signatures. Specifically, this project aims to create and experimentally validate a PDE-ODE model-based approach for the design of estimators that rely on melt-front measurements alone to estimate temperature profiles and feedback controllers that use these estimates to robustly regulate melt pool size and cooling rates for varying layer geometry and local thermal properties.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.

该奖项将允许开发新的分析框架，以改善金属增材制造工艺的控制。增材制造技术彻底改变了设计和制造，通过数字模型创建三维物体，可以快速定制生产和产品开发。尽管过去二十年付出了巨大的努力，但在构建过程中仍经常出现几何不准确、裂纹和微观结构不均匀等制造缺陷。这些问题主要由多种因素引起，包括激光束和粉末床之间的相互作用，这些因素会影响制造过程中的热历史。本研究旨在通过理论和实验研究改进功能性金属最终产品的制造，从而更好地调节和控制温度冷却速率和熔池宽度，这两者都是打印复杂几何形状的关键因素。该项目的成功完成将对航空航天和国防等高价值行业使用金属增材制造制造功能部件产生积极影响。通过全面的推广和教育计划，本科生和研究生将参与将先进制造与系统理论相结合的跨学科学习活动。金属增材制造工艺（例如选择性激光熔化）的现有控制方法建立在近似常微分方程（ODE）模型或集总参数假设的基础上。这些简化的方法不能精确捕获温度场的空间分布性质，并且完全忽略相变动力学、层几何形状的影响以及先前的激光通过。相反，具有相变动力学的预测偏微分方程（PDE）模型对于实时控制来说过于复杂。通过这项研究工作，我们提供了基于基本守恒定律的面向控制的 PDE-ODE 模型，该模型结合了相变动力学，并能够实时估计和控制过程特征。具体来说，该项目旨在创建并通过实验验证基于 PDE-ODE 模型的方法，用于设计估计器，该估计器仅依靠熔体前沿测量来估计温度分布，并使用这些估计来稳健地调节熔池尺寸和冷却的反馈控制器该奖项反映了 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。