Heterogeneous sensor-based condition monitoring in directed energy deposition
Heterogeneous sensor-based condition monitoring in directed energy deposition
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DOI：
10.1016/j.addma.2019.100916
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发表时间：
2019-12-01
影响因子：
11
通讯作者：
Rao, Prahalada
中科院分区：
文献类型：
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作者：
Montazeri, Mohammad;Nassar, Abdalla R.;Rao, Prahalada
本工作的目的是原位检测定向能沉积(DED)添加制造(AM)钛合金(Ti-6Al-4V)零件中未熔合缺陷的发生情况。我们使用了两种类型的过程中传感器的数据，即光谱仪和光学相机，它们被集成到Optomec MR-7DED机器中。这两个传感器都专注于捕捉熔池区域周围的动态现象。为了检测融合缺失缺陷，我们调用图的Kronecker积的概念对来自过程中传感器的数据进行融合(组合)。随后，我们使用从图Kronecker产品获得的特征作为机器学习算法的输入，以预测从测试部件的离线X射线计算机断层扫描获得的层内未融合缺陷的平均长度的严重性(类别或级别)。我们证明，对于两级分类方案，缺乏融合缺陷的严重程度的统计保真度(F-Score)接近85%，对于三级分类方案，接近70%。因此，这项工作展示了使用异质的过程中传感和在线数据分析来原位检测DED金属AM工艺中的缺陷。
The objective of this work is to detect in situ the occurrence of lack-of-fusion defects in titanium alloy (Ti-6Al-4V) parts made using directed energy deposition (DED) additive manufacturing (AM). We use data from two types of in-process sensors, namely, a spectrometer and an optical camera which are integrated into an Optomec MR-7 DED machine. Both sensors are focused on capturing the dynamic phenomena around the melt pool region. To detect lack-of-fusion defects, we fuse (combine) the data from the in-process sensors invoking the concept of Kronecker product of graphs. Subsequently, we use the features derived from the graph Kronecker product as inputs to a machine learning algorithm to predict the severity (class or level) of average length of lack-of-fusion defects within a layer, which is obtained from offline X-ray computed tomography of the test parts. We demonstrate that the severity of lack-of-fusion defects is classified with statistical fidelity (F-score) close to 85% for a two-level classification scenario, and approximately 70% for a three-level classification scenario. Accordingly, this work demonstrates the use of heterogeneous in-process sensing and online data analytics for in situ detection of defects in DED metal AM process.