Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion - A single-track study
Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion - A single-track study
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激光粉末床熔合质量保证的异构传感和科学机器学习——单轨研究
DOI：
10.1016/j.addma.2020.101659
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发表时间：
2020-12-01
影响因子：
11
通讯作者：
Rao, Prahalada
中科院分区：
文献类型：
--
作者：
Gaikwad, Aniruddha;Giera, Brian;Rao, Prahalada
激光粉末床融合（LPBF）是一种主要的金属增材制造技术，由于其能够制造复杂几何形状的零件，因此受益于大量的学术研究和工业投资。尽管LPBF的广泛使用，仍然存在对过程监控的需求，以确保可靠的零件生产并减少构建后的质量评估。为此，我们开发和评估基于机器学习的预测模型，使用高度地图导出的单轨道质量指标以及在各种激光功率和激光速度设置下收集的高温计和高速摄像机数据。我们提取物理直观的低级别功能代表的熔池动力学从这些传感方式，并探讨这些变化与线性能量密度。我们发现我们的序列决策分析神经网络（SeDANN）模型-一种结合物理过程洞察的科学机器学习模型-在准确性和速度方面优于其他纯数据驱动的黑盒模型。SeDANN的科学架构的数据管理和适应性的一般方法应该有利于LPBF系统，具有不断发展的传感模式和构建后质量测量套件。
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.