  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.
