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，以改善金属添加剂制造过程的控制。增材制造技术彻底改变了设计和制造，从而通过创建数字模型的三维对象来快速自定义生产和产品开发。尽管在过去的二十年中做出了巨大的努力，但在建造过程中经常出现制造缺陷，例如几何不准确，裂缝和微观结构不均匀性。这些问题主要源于各种因素，包括影响制造过程中热史的激光束和粉末床之间的相互作用。这项研究旨在通过理论和实验研究来改善功能性金属最终产品的生产，这将使能够更好地调节和控制温度冷却速率和融化池的宽度，这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金属添加剂制造的使用积极影响，用于在航空航天和防御等高价值行业中制造功能零件。通过彻底的宣传和教育计划，本科生和研究生将参加跨学科学习活动，使制造业与系统理论相结合。现有的金属添加剂制造过程（例如选择性激光熔化）的控制方法建立在近似的普通微分方程（ODE）模型或集体参数假设的基础上。这些简化的方法不能精确地捕获温度场的空间分布性质，并且完全忽略了相变动力学，层几何形状的效果以及先前的激光通过。相反，具有相变动力学的预测性部分微分方程（PDE）模型对于实时控制来说太复杂了。通过这项研究工作，我们提供了以控制为导向的PDE-ODE模型，这些模型源自基本的保护定律，这些模型结合了相变动力，并实现了实时估算和过程特征的控制。具体而言，该项目旨在创建和实验验证一种基于PDE的模型方法，用于设计估计器的设计，该方法仅依靠熔体前的测量值来估算使用这些估计值的温度概况和反馈控制器，以稳健地调节融化池尺寸，并通过多种质量授予nsf的质量来调节层次的大小和冷却速率。智力优点和更广泛的影响审查标准。