Recent applications of machine learning in metal additive manufacturing (MAM)
have demonstrated significant potential in addressing critical barriers to the
widespread adoption of MAM technology. Recent research in this field emphasizes
the importance of utilizing melt pool signatures for real-time defect
prediction. While high-quality melt pool image data holds the promise of
enabling precise predictions, there has been limited exploration into the
utilization of cutting-edge spatiotemporal models that can harness the inherent
transient and sequential characteristics of the additive manufacturing process.
This research introduces and puts into practice some of the leading deep
spatiotemporal learning models that can be adapted for the classification of
melt pool image streams originating from various materials, systems, and
applications. Specifically, it investigates two-stream networks comprising
spatial and temporal streams, a recurrent spatial network, and a factorized 3D
convolutional neural network. The capacity of these models to generalize when
exposed to perturbations in melt pool image data is examined using data
perturbation techniques grounded in real-world process scenarios. The
implemented architectures demonstrate the ability to capture the spatiotemporal
features of melt pool image sequences. However, among these models, only the
Kinetics400 pre-trained SlowFast network, categorized as a two-stream network,
exhibits robust generalization capabilities in the presence of data
perturbations.

机器学习在金属增材制造中的应用研究表明，利用熔池特征进行实时缺陷预测是解决普及金属增材制造技术的关键障碍之一，本研究实践了基于新颖时空模型的深度学习，用于分类来自不同材料、系统和应用的熔池图像序列，并发现只有 Kinetics400 预训练的 SlowFast 双流网络能够在数据扰动下具备鲁棒的泛化能力。