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

通过知识辅助机器学习进行金属增材制造的工艺优化和产品设计

• 批准号: RGPIN-2019-06601
• 负责人: Wang, Gaofeng
• 金额: $ 2.84万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716201
• 关键词: Process; Optimization; Product; Design; Metal

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.

增材制造（AM）是一种颠覆性技术，可以制造复杂形状，定制零件，并直接制造物体，而无需昂贵的工具。 在众多的增材制造技术中，金属增材制造因其潜在的工业应用前景而备受关注。 金属增材制造的关键技术挑战包括增材制造的工艺建模、工艺参数优化和产品设计。 工艺参数（例如，激光功率、激光速度和层厚度）显著影响最终的机械性能，例如孔隙率、拉伸强度和硬度。 为了理解工艺参数和产品机械性能之间的关系，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微分方程来控制基本的热机械过程。 由于金属AM工艺的复杂性，目前还缺乏可靠的模拟工具。 数据驱动模型来自不同工艺参数设置的实验。然而，这种方法需要指数增长的实验数量，因为参数的数量变得相对较大，这是金属AM的情况。实验成本很快变得太高。 此外，这种方法需要应用于每一种不同的技术，甚至每一台机器，这阻碍了它的实际使用。 拓扑优化结果的非参数化，需要在AM之前进行繁琐的人工处理，这是AM设计的另一个挑战。 本课题旨在研究和开发金属AM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的人工神经网络来建立一个数据驱动的模型，减少实验，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。将定义一个集成和简化的混合模型，该模型可以在不同的机器和技术之间轻松转移。将开发专用的优化算法，以优化激光束熔化和激光束沉积金属AM机器的工艺参数。 我还建议使用原始几何，如孔，圆柱和三角形的拓扑优化的基本元素，使拓扑优化的输出是参数化几何，可以直接用于优化和AM。 该提案的成果将是方法和软件原型，可以生成参数拓扑和几何形状，对金属AM工艺进行建模，并优化工艺参数，以实现金属AM的最高效率，最低成本或最高产品质量。这项研究将填补文献中的空白，并满足一个新兴的60亿美元产业的需求。 总共有4名博士，2名硕士和5名本科生将通过该研究计划直接培训。