Monitoring and Prediction of Porosity in Laser Powder Bed Fusion using Physics-informed Meltpool Signatures and Machine Learning
Monitoring and Prediction of Porosity in Laser Powder Bed Fusion using Physics-informed Meltpool Signatures and Machine Learning
复制标题
DOI：
10.1016/j.jmatprotec.2022.117550
复制
发表时间：
2022-03
影响因子：
6.3
通讯作者：
Z. Smoqi;A. Gaikwad;Ben Bevans;Md Humaun Kobir;J. Craig;Alan Abul-Haj;A. Peralta;Prahalada K. Rao
中科院分区：
文献类型：
--
作者：
Z. Smoqi;A. Gaikwad;Ben Bevans;Md Humaun Kobir;J. Craig;Alan Abul-Haj;A. Peralta;Prahalada K. Rao
在这项工作中，我们完成了激光粉末床熔融（LPBF）增材制造过程中的孔隙率监测和预测。这一目标是通过从原位双波长成像高温计中提取物理信息的熔池签名，然后通过计算上易于处理的机器学习方法分析这些签名来实现的。由于随机原因，尽管对工艺条件进行了广泛的优化，但LPBF中仍会出现孔隙。因此，必须使用原位传感器持续监测该过程，以检测和减轻初始孔隙形成。在这项工作中，通过改变激光功率和扫描速度，构建了具有受控孔隙率的高立方体形状部件（10 mm × 10 mm × 137 mm，材料ATI 718 Plus）。该测试导致了各种类型的孔隙率，例如未熔合和小孔形成，在部件中具有不同程度的严重性。使用安装在机器中的双波长成像高温计连续监测熔池。物理直观的过程签名，如熔池长度，温度分布，和喷出物（飞溅）的特点，从熔池图像中提取。随后，相对简单的机器学习模型，例如，K-最近邻，进行了培训，以预测作为这些物理信息的熔池签名的函数的孔隙度的严重程度和类型。这些模型的预测准确率超过95%（统计F1分数）。同样的分析是用一个复杂的黑盒深度学习卷积神经网络进行的，该网络直接使用熔池图像而不是物理信息特征。卷积神经网络产生的F1分数在89- 97%之间。这些结果表明，在一个简单的机器学习模型中使用实用的、基于物理信息的熔池特征，与使用复杂的、计算要求高的黑盒深度学习模型一样，可以有效地预测LPBF中的缺陷。
In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. These results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.