利用高速同步X射线成像和热成像技术，结合多物理场模拟，研究人员发现了Ti-6Al-4V激光粉末床融合中的两种类型的锁孔振荡。利用机器学习，课题组开发了一种检测随机锁孔孔隙生成事件的方法，该方法具有亚毫秒级的时间分辨率和近乎完美的预测率。operando X射线成像所实现的高度准确的数据标记，展示了一种在商业系统中简单而实用的方法。
据介绍，多孔性缺陷是目前阻碍激光金属增材制造技术广泛采用的主要因素。当输入过多的激光能量形成不稳定的蒸汽凹陷区(锁孔)时，就会出现常见的孔隙。
附：英文原文
Title: Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion
Author: Zhongshu Ren, Lin Gao, Samuel J. Clark, Kamel Fezzaa, Pavel Shevchenko, Ann Choi, Wes Everhart, Anthony D. Rollett, Lianyi Chen, Tao Sun
Issue&Volume: 2023-01-06
Abstract: Porosity defects are currently a major factor that hinders the widespread adoption of laser-based metal additive manufacturing technologies. One common porosity occurs when an unstable vapor depression zone (keyhole) forms because of excess laser energy input. With simultaneous high-speed synchrotron x-ray imaging and thermal imaging, coupled with multiphysics simulations, we discovered two types of keyhole oscillation in laser powder bed fusion of Ti-6Al-4V. Amplifying this understanding with machine learning, we developed an approach for detecting the stochastic keyhole porosity generation events with submillisecond temporal resolution and near-perfect prediction rate. The highly accurate data labeling enabled by operando x-ray imaging allowed us to demonstrate a facile and practical way to adopt our approach in commercial systems.
DOI: add4667
Source: https://www.science.org/doi/10.1126/science.add4667