During the past decade, metal additive manufacturing (MAM) has experienced significant developments and gained much attention due to its ability to fabricate complex parts, manufacture products with functionally graded materials, minimize waste, and enable low-cost customization. Despite these advantages, predicting the impact of processing parameters on the characteristics of an MAM printed clad is challenging due to the complex nature of MAM processes. Machine learning (ML) techniques can help connect the physics underlying the process and processing parameters to the clad characteristics. In this study, we introduce a hybrid approach which involves utilizing the data provided by a calibrated multi-physics computational fluid dynamic (CFD) model and experimental research for preparing the essential big dataset, and then uses a comprehensive framework consisting of various ML models to predict and understand clad characteristics. We first compile an extensive dataset by fusing experimental data into the data generated using the developed CFD model for this study. This dataset comprises critical clad characteristics, including geometrical features such as width, height, and depth, labels identifying clad quality, and processing parameters. Second, we use two sets of processing parameters for training the ML models: machine setting parameters and physics-aware parameters, along with versatile ML models and reliable evaluation metrics to create a comprehensive and scalable learning framework for predicting clad geometry and quality. This framework can serve as a basis for clad characteristics control and process optimization. The framework resolves many challenges of conventional modeling methods in MAM by solving t the issue of data scarcity using a hybrid approach and introducing an efficient, accurate, and scalable platform for clad characteristics prediction and optimization.

翻译：过去十年间，金属增材制造（MAM）因其能够制造复杂零件、生产功能梯度材料产品、减少浪费并实现低成本定制化而取得显著发展并备受关注。尽管具备这些优势，但由于MAM过程的复杂本质，预测工艺参数对打印熔覆层特征的影响仍具有挑战性。机器学习（ML）技术能够将工艺物理机制与工艺参数之间的关联映射至熔覆层特征。本研究提出一种混合方法：首先利用校准的多物理场计算流体动力学（CFD）模型数据与实验研究构建关键大数据集，然后采用包含多种ML模型的综合框架来预测和理解熔覆层特征。我们首先将实验数据融合至本研究开发的CFD模型生成数据中，编制了包含关键熔覆层特征（如宽度、高度、深度等几何特征）、熔覆质量标签及工艺参数的综合数据集。其次，使用两类工艺参数（机器设定参数与物理感知参数）训练ML模型，结合多样化ML模型与可靠评估指标，构建了用于预测熔覆层几何形貌与质量的全面可扩展学习框架。该框架可作为熔覆层特征控制与工艺优化的基础，通过混合方法解决数据稀缺问题，并引入高效、准确、可扩展的预测优化平台，有效克服了MAM传统建模方法的多项挑战。