Layerwise In-Process Quality Monitoring in Laser Powder Bed Fusion
Layerwise In-Process Quality Monitoring in Laser Powder Bed Fusion
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DOI：
10.1115/msec2018-6477
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Farhad Imani;A. Gaikwad;M. Montazeri;Prahalada K. Rao;Hui Yang;E. Reutzel
中科院分区：
文献类型：
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作者：
Farhad Imani;A. Gaikwad;M. Montazeri;Prahalada K. Rao;Hui Yang;E. Reutzel
这项工作的目标是了解工艺条件对激光粉末床熔融（LPBF）增材制造（AM）工艺中零件孔隙率的影响，随后，从过程传感器数据中检测导致孔隙率的工艺条件的开始。为了实现这一目标，本工作的目标有两个：(1)将孔隙的数量、大小和位置量化为三个LPBF工艺参数的函数，即舱口间距(H)、激光速度(V)和激光功率(P)。(2)通过分析过程中逐层光学图像，利用多重分形和光谱图理论特征，监测和识别容易导致孔隙的工艺条件。这一点很重要，因为孔隙率对LPBF部件的功能完整性有重大影响，例如疲劳寿命。此外，将过程条件与传感器特征和缺陷联系起来是实现LPBF过程中质量保证的第一步。为了实现第一个目标，在商用LPBF机器（EOS M280）上，在不同的H、V和P设置下，构建了直径10 mm ×高25 mm的钛合金（Ti-6Al-4V）测试气缸。基于x射线计算机断层扫描（XCT）图像，量化了这些参数对孔隙数量、大小和位置的影响。为了实现第二个目标，在构建零件时获得粉末床的分层光学图像。从每个测试部件的逐层图像中提取光谱图理论和多重分形特征。随后，使用机器学习方法将这些特征与工艺参数联系起来。通过这些基于图像的特征，识别零件制造的工艺条件，统计保真度超过80% （F-score）。
The goal of this work is to understand the effect of process conditions on part porosity in laser powder bed fusion (LPBF) Additive Manufacturing (AM) process, and subsequently, detect the onset of process conditions that lead to porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are two-fold: (1) Quantify the count (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P). (2) Monitor and identify process conditions that are liable to cause porosity through analysis of in-process layer-by-layer optical images of the build invoking multifractal and spectral graph theoretic features. This is important because porosity has a significant impact on the functional integrity of LPBF parts, such as fatigue life. Furthermore, linking process conditions to sensor signatures and defects is the first-step towards in-process quality assurance in LPBF. To achieve the first objective, titanium alloy (Ti-6Al-4V) test cylinders of 10 mm diameter × 25 mm height were built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these parameters on count, size and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features, process conditions under which the parts were built was identified with the statistical fidelity over 80% (F-score).