In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes
In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes
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DOI：
10.1080/24725854.2017.1417656
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发表时间：
2019-01-01
影响因子：
2.6
通讯作者：
Bian, Linkan
中科院分区：
文献类型：
--
作者：
Khanzadeh, Mojtaba;Chowdhury, Sudipta;Bian, Linkan
实施定向能量沉积（DED）增材制造（AM）的一个主要挑战是缺乏对其潜在工艺-结构-性能关系的理解。使用DED技术制造的零件可能过于不一致和不可靠，无法满足许多工业应用的严格要求。本研究的目的是表征基本的热物理动力学的DED过程中，捕获的熔池信号，并预测在构建过程中的孔隙率。在此，我们提出了一种新的孔隙度预测方法的基础上的温度分布的顶部表面的熔池作为AM部分正在建立。自组织映射（SOM），然后用于进一步分析的二维熔池图像流，以确定相似和不相似的熔池。X射线断层扫描用于实验定位Ti-6Al-4V薄壁试样内的孔隙度，然后将其与基于熔池分析的预测孔隙度位置进行比较。结果表明，所提出的方法的基础上的熔池的温度分布是能够预测孔隙的位置几乎96%的时间时，选择适当的SOM模型使用的热剖面。结果也与以前的研究，只集中在形状和大小的熔池。我们发现，纳入热分布显着提高孔隙度预测的准确性。基于熔池轮廓的所提出的方法的意义在于，这可以引导朝向原位监测的方式，并且最小化甚至消除AM部件内的孔隙。
One major challenge of implementing Directed Energy Deposition (DED) Additive Manufacturing (AM) for production is the lack of understanding of its underlying process-structure-property relationship. Parts manufactured using the DED technologies may be too inconsistent and unreliable to meet the stringent requirements for many industrial applications. The objective of this research is to characterize the underlying thermo-physical dynamics of the DED process, captured by melt pool signals, and predict porosity during the build. Herein we propose a novel porosity prediction method based on the temperature distribution of the top surface of the melt pool as an AM part is being built. Self-Organizing Maps (SOMs) are then used to further analyze the two-dimensional melt pool image streams to identify similar and dissimilar melt pools. X-ray tomography is used to experimentally locate porosity within the Ti-6Al-4V thin-wall specimen, which is then compared with predicted porosity locations based on the melt pool analysis. Results show that the proposed method based on the temperature distribution of the melt pool is able to predict the location of porosity almost 96% of the time when the appropriate SOM model using a thermal profile is selected. Results are also compared with a previous study, that focuses only on the shape and size of the melt pool. We find that the incorporation of thermal distribution significantly improves the accuracy of porosity prediction. The significance of the proposed methodology based on the melt pool profiles is that this can lead the way toward in situ monitoring and minimize or even eliminate pores within the AM parts.