Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam
Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam
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DOI：
10.1016/j.addma.2022.102736
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发表时间：
2022-03
影响因子：
11
通讯作者：
Y. Gui;K. Aoyagi;Huakang Bian;A. Chiba
中科院分区：
文献类型：
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作者：
Y. Gui;K. Aoyagi;Huakang Bian;A. Chiba
在使用电子束（PBF-EB）的粉末床熔融型增材制造中，各种工艺参数对所制造部件的性能具有显著影响。为了扩大PBF-EB技术在材料工业中的应用，其中一个问题是在工艺过程中产生内部缺陷（孔隙，未熔化的粉末等）。在这项研究中，我们确定了一个定量标准（Sdr < 0.015为均匀表面; Sa ≥ 80 µm为不均匀表面; Sdr ≥ 0.015且Sa < 80 µm为多孔表面），用于根据表面平整度对表面质量进行分类，我们发现不同的表面质量（均匀、不均匀和多孔）包括不同类型的内部缺陷。具有均匀表面的部件没有内部缺陷，并且具有最高的密度（7.7962 g/cm 3）。具有不平坦表面的零件由于能量输入过多而具有大量球形孔隙，而具有多孔表面的零件由于能量输入不足而具有相当数量的不规则形状的缺陷和未熔化的粉末。当能量输入过高时，马兰戈尼效应、蒸气反冲压力和电子束搅动的组合导致液体的高速流动，这倾向于形成凸起，从而导致不平坦的表面。相反，如果能量输入太低，熔池的深度太小而不能穿透粉末层的厚度，导致粉末在层底部的不完全熔化以及由于层之间缺乏熔合而形成缺陷。此外，将Logistic回归、支持向量机、决策树、XGBoost和朴素贝叶斯等5种机器学习技术应用于S30 C合金PBF-EB工艺参数优化。支持向量机具有最高的模型性能，我们用它来构建对应于内部缺陷的加工图，并确定S30 C合金的PBF-EB工艺窗口。S30 C合金的最佳PBF-EB工艺参数范围为：电流2.5 ~ 10 mA，扫描速度200-1000 mm/s，线宽0.11-0.25 mm;或电流2.5 ~ 10 mA，扫描速度200-750 mm/s，线宽0.27-0.33 mm。提出了一种新的PBF-EB制造工艺图的构建框架，为加速PBF-EB制造无内部缺陷零件提供了一种有效的方法。
In powder bed fusion type additive manufacturing using an electron beam (PBF-EB), various process parameters have a significant influence on the performance of manufactured parts. To expand the use of PBF-EB technology in the material industry, one of the problems is the generation of internal defects (pores, unmelted powder, among others) during the process. In this study, we determined a quantitative criterion (Sdr < 0.015 for an even surface; Sa ≥ 80 µm for an uneven surface; Sdr ≥ 0.015 and Sa < 80 µm for a porous surface) for classifying surface quality based on surface flatness, and we revealed that different surface qualities (even, uneven, and porous) include different types of internal defects. The parts with even surfaces are free of internal defects and have the highest density(7.7962 g/cm3). Parts with uneven surfaces have a large number of spherical pores owing to their excessive energy input, while parts with porous surfaces have a considerable number of irregularly shaped defects and unmelted powders owing to their insufficient energy input. When the energy input is excessively high, the combination of the Marangoni effect, vapor recoil pressure, and electron beam agitation leads to a high velocity flow of liquid, which tends to form bumps, resulting in an uneven surface. Conversely, if the energy input is too low, the depth of the melt pool is too small to penetrate the thickness of the powder layers, resulting in incomplete melting of the powder at the bottom of the layers and the formation of defects due to lack of fusion between the layers. In addition, five types of machine learning technologies (logistic regression, support vector machine, decision tree, XGBoost, and naive Bayes) were applied to the PBF-EB process parameters optimization of the S30C alloy. A support vector machine has the highest model performance, and we use it to construct a processing map corresponding to the internal defects and determine the PBF-EB process window for the S30C alloy. The optimal PBF-EB process parameter ranges for S30C alloy were predicted as follows: current = 2.5–10 mA, scan speed = 200–1000 mm/s, line offset = 0.11–0.25 mm, or current = 2.5–10 mA, scan speed = 200–750 mm/s, line offset = 0.27–0.33 mm. Moreover, a new framework for constructing a process map of PBF-EB fabricated parts was proposed, which is an effective method to accelerate PBF-EB for manufacturing parts without internal defects.