Modeling and Control for Laser Based Additive Manufacturing Processes

添加制造，也被称为3D打印，以生产复杂形状的能力而闻名，与传统加工相比，大大减少了制造时间和材料。该项目专注于使用强大的激光将一池金属粉末熔化到不断增长的工作件上的添加制造工艺。对熔池附近温度的不准确了解可能会导致激光能量的使用过多或过少，从而导致最终零件的尺寸错误。3D打印组件的复杂几何形状可能会使准确了解温度变得困难。本项目研究使用前馈控制来解决这个问题，通过增加熔池的实时传感器测量，使用z系列预先计算的热模型来表示零件形成时的温度。这一策略预测了输入热能的必要变化，因此比仅在错误发生后才对其进行响应具有更高的准确性。该项目的成果将具有潜在的应用范围，从定制的医疗植入物到航空航天燃料喷嘴，再到涡轮叶片修复。该项目将推动金属部件添加剂制造的最先进水平。该项目还通过大学层面的倡议，特别是宾夕法尼亚州立大学的女性科学与工程研究(WISER)和少数民族本科生研究经验(MURE)计划，以及高中教师添加剂制造夏令营的课程开发，支持招募妇女和少数族裔。尽管添加剂制造具有巨大的潜力，但由于过程中的多重挑战，包括零件几何精度、加工零件的机械和材料性能以及表面粗糙度，添加剂制造尚未在工业中得到广泛采用。为了解决这些问题，需要在建模和控制方面有基本的认识和先进的技术，以提高加法制造的精度和过程稳定性。该项目研究基于激光的添加制造过程的新的建模方法和控制算法，特别是定向能沉积。这项研究为添加剂制造科学界提供的智力意义包括：1)基于物理的、面向控制的动态模型，该模型解释了3D零件几何和热历史。该模型将具有高保真度，但仍适用于实时多变量控制；以及2)非线性多输入、多输出控制范例。该控制方法利用了可加性制造过程动力学的特殊非线性结构，不需要线性化，还解决了输入/状态约束、性能优化和对不确定模型参数的鲁棒性的约束问题。研究小组将进行实验测试，以评估建模和控制方法的有效性。

Additive manufacturing, also called 3D printing, is well known for its ability to produce complex shapes, with great reductions in manufacturing time and material over traditional machining. This project focuses on additive manufacturing processes that use a powerful laser to melt a pool of metal powder onto a growing work piece. Inaccurate knowledge of the temperature in the vicinity of the melt pool can lead to use of too much or too little laser energy, and thence to errors in the dimensions of the final part. The complex geometry of 3D-printed components can make accurate knowledge of temperature difficult. This project studies the use of feedforward control to address this problem, by augmenting real-time sensor measurement of the melt pool with z family of pre-computed thermal models representing the part as it is formed. This strategy anticipates necessary changes in input thermal energy, and thereby allows much higher accuracy than responding to errors only after they occur. Results from this project will have potential applications ranging from customized medical implants to aerospace fuel nozzles to turbine blade repair. This project will advance the state of the art in additive manufacturing for metal components. The project also supports recruitment of women and minorities through university-level initiatives, specifically Penn State's Women in Science and Engineering Research (WISER)and Minority Undergraduate Research Experience (MURE) programs, and curriculum development for an additive manufacturing summer camp for high school teachers.Despite its huge potential, additive manufacturing has not yet achieved widespread adoption in industry due to multiple challenges in the process, including accuracy of part geometry, mechanical and material properties of the processed part, and surface roughness. To resolve these issues, fundamental understanding and advanced technologies for modeling and control are in demand to improve the accuracy and process stability of additive manufacturing. This project investigates novel modeling methodologies and control algorithms for laser-based additive manufacturing processes, specifically for directed energy deposition. Intellectual significance offered by the research to the additive manufacturing scientific community include: 1) A physics-based, control-oriented dynamic model that accounts for 3D part geometry and thermal history. This model will have high fidelity yet still be amenable for real-time multivariable control; and 2) A nonlinear multi-input, multi-output control paradigm. This control paradigm takes advantage of the special nonlinear structure of the additive manufacturing process dynamics without resorting to linearization, and also addresses constraints for input/state constraints, performance optimization and robustness with respect to uncertain model parameters. Experimental tests will be performed by the research team to evaluate the effectiveness of the modeling and control methodologies.