Laser Powder Bed Fusion (LPBF) is the predominant metal additive manufacturing technique that benefits from a significant body of academic study and industrial investment given its ability to create complex geometry parts. Despite LPBF’s widespread use, there still exists a need for process monitoring to ensure reliable part production and reduce post-build quality assessments. Towards this end, we develop and evaluate machine learning-based predictive models using height map-derived quality metrics for single tracks and the accompanying pyrometer and high-speed video camera data collected under a wide range of laser power and laser velocity settings. We extract physically intuitive low-level features representative of the meltpool dynamics from these sensing modalities and explore how these vary with the linear energy density. We find our Sequential Decision Analysis Neural Network (SeDANN) model – a scientific machine learning model that incorporates physical process insights – outperforms other purely data-driven black box models in both accuracy and speed. The general approach to data curation and adaptable nature of SeDANN’s scientifically informed architecture should benefit LPBF systems with an evolving suite of sensing modalities and post-build quality measurements.

激光粉末床熔合（LPBF）是主要的金属增材制造技术，由于其能够创建复杂的几何形状零件，因此得益于大量的学术研究和工业投资，该技术得到了广泛的应用。尽管LPBF的广泛使用，但仍需要进行过程监控以确保可靠的零件生产并减少制造后的质量评估。为此，我们使用源自高度图的单轨质量度量以及随附的高温计和在广泛的激光功率和激光速度设置下收集的高速摄像机数据，开发和评估基于机器学习的预测模型。我们从这些传感方式中提取出代表熔池动力学的直观直观的低层特征，并探索它们如何随线性能量密度而变化。融合了物理过程洞察力的科学机器学习模型–在准确性和速度上都优于其他纯粹由数据驱动的黑匣子模型。SeDANN具有科学依据的体系结构的通用数据管理方法和适应性性质，应通过不断发展的传感模式和建后质量测量套件，使LPBF系统受益。