Process Optimization and Product Design for Metal Additive Manufacturing via Knowledge-Assisted Machine Learning

添加制造(AM)是一种颠覆性技术，它可以制造复杂的形状、定制的部件，并且无需昂贵的工具就可以直接制造对象。在众多的AM技术中，金属AM因其在工业上的广泛应用而备受关注。金属AM的关键技术挑战包括工艺建模、工艺参数优化和产品设计工艺参数(如激光功率、激光速度和涂层厚度)显著影响最终的机械性能，如气孔率、抗拉强度和硬度。为了了解工艺参数与产品力学性能之间的关系，人们建立了基于物理的模型或数据驱动的模型。基于物理的模型使用控制基本热机械过程的微分方程式。由于工艺的高度复杂性，目前尚缺乏可信可靠的金属AM技术仿真工具。数据驱动模型是从不同工艺参数设置下的实验得出的。然而，随着参数的数量变得相对较大，该方法需要指数增长的实验数量，这是对于金属AM的情况实验成本很快就变得太高了。此外，这种方法需要应用于每一种不同的技术，甚至每台机器，这阻碍了它的实际使用。AM设计中的另一个挑战是，拓扑优化结果不是参数的，需要在AM之前进行繁琐的人工处理*本提案旨在研究和开发金属AM的工艺参数优化和产品设计方法，以应对上述挑战。我建议使用知识辅助人工神经网络来建立一个实验较少的数据驱动模型，这反过来有助于微调基于物理的模型，并定义关键参数及其有效值范围。将定义一种集成和简化的混合模型，以便在不同的机器和技术之间轻松转移。将开发专门的优化算法来优化激光熔化和激光沉积金属AM机床的工艺参数。我还建议使用孔、圆柱和三角形等原始几何作为拓扑优化的基本元素，以便拓扑优化的输出是可直接用于优化和AM的参数几何。*建议的结果将是方法和软件原型，该方法和软件原型可以生成参数拓扑和几何，模拟金属AM过程，并从金属AM优化过程参数以实现最高效率、最低成本或最高产品质量这项研究将填补文献中的空白，并满足一个价值60亿美元的新兴行业的需求。共有4名博士、2名硕士和5名本科生将通过该研究计划直接接受培训。

Additive Manufacturing (AM) is a disruptive technology that can fabricate complex shapes, customized parts, and directly make objects without expensive tooling. Among various AM technologies, metal AM has attracted the most attention due to the potentially wide application in industry. Key technical challenges to metal AM include process modeling, process parameter optimization, and product design for AM. Process parameters (e.g., laser power, laser velocity, and layer thickness) influence the final mechanical properties such as porosity, tensile strength, and hardness significantly. To understand the relationship between the process parameters and the product mechanical properties, one builds physics-based models or data-driven models. Physics-based models use differential equations that govern the underlying thermomechanical process. Due to high process complexity, there are still lack of credible and reliable simulation tools for metal AM technologies. Data-driven models are derived from experiments on different process parameter settings. This approach, however, needs an exponentially growing number of experiments as the number of parameters becomes relatively large, which is the case for metal AM. The experiment costs soon become too high. Moreover, this approach needs to be applied for every different technology and even every machine, which prevents its practice use. Another challenge in design for AM is that the topology optimization results are not parametric and need tedious manual processing before AM. ******This proposal aims to research and develop methods for process parameter optimization and product design for metal AM to address the above challenges. I propose to use knowledge-assisted artificial neural networks to build a data-driven model with fewer experiments, which in return help to fine-tune a physics-based model and define key parameters and their effective value ranges. An integrated and simplified hybrid model will be defined that is easily transferable between different machines and technologies. Dedicated optimization algorithms are to be developed to optimize process parameters for both Laser Beam Melting and Laser Beam Deposition metal AM machines. I also propose to use primitive geometries such as holes, cylinders and triangles to be the basic elements for topology optimization so that the output of topology optimization is parametric geometry that can be directly used for optimization and AM.******The outcome of the proposal will be methods and a software prototype that can generate parametric topology and geometry, model metal AM processes, and optimize process parameters for maximum efficiency, lowest cost, or highest product quality from metal AM. This research will fill a gap in the literature and meet the needs of an emerging US$6 billion industry. In total, 4 PhD, 2 MSc and 5 undergraduate students will be directly trained via this research program.