Modeling and Control for Laser Based Additive Manufacturing Processes

增材制造，也称为3D打印，以其生产复杂形状的能力而闻名，与传统加工相比，制造时间和材料大大减少。该项目专注于增材制造工艺，使用强大的激光将金属粉末池熔化到正在生长的工件上。对熔池附近温度的不准确了解可能导致使用过多或过少的激光能量，从而导致最终零件尺寸的误差。3d打印部件的复杂几何形状使精确的温度知识变得困难。本项目研究使用前馈控制来解决这一问题，通过使用z系列预先计算的热模型来增加对熔池的实时传感器测量，这些模型代表了零件形成时的情况。这种策略可以预测输入热能的必要变化，因此比只在错误发生后才做出响应的精度要高得多。该项目的成果将具有潜在的应用范围，从定制医疗植入物到航空燃料喷嘴到涡轮叶片修复。该项目将推动金属部件增材制造技术的发展。该项目还通过大学层面的举措支持招募女性和少数族裔，特别是宾夕法尼亚州立大学的科学与工程研究女性（WISER）和少数族裔本科生研究经验（MURE）项目，以及为高中教师开设增材制造夏令营的课程开发。尽管具有巨大的潜力，但由于加工过程中的多重挑战，包括零件几何形状的精度、被加工零件的机械和材料性能以及表面粗糙度，增材制造尚未在工业中得到广泛应用。为了解决这些问题，需要对建模和控制的基本理解和先进技术，以提高增材制造的精度和过程稳定性。该项目研究了基于激光的增材制造工艺的新型建模方法和控制算法，特别是定向能沉积。该研究为增材制造科学界提供的知识意义包括：1)基于物理的、面向控制的动态模型，该模型考虑了3D零件的几何形状和热历史。该模型具有较高的保真度，但仍能适应实时多变量控制；2)非线性多输入、多输出控制范式。该控制模式利用了增材制造过程动力学的特殊非线性结构，而无需线性化，并且还解决了输入/状态约束、性能优化和不确定模型参数的鲁棒性约束。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.