Effect of parameters for dul-contours on the surface forming of nickel-based superalloys fabricated by LPBF
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摘要:
激光粉末床熔融(laser powder bed fusion，LPBF)增材制造技术广泛用于航空航天领域复杂结构的镍基高温合金零件的一体化制造，但是其粗糙度问题限制了该项技术的应用. 基于此，通过采用双轮廓扫描策略优化表面成形质量，并研究轮廓参数的热输入对表面成形质量及微观组织、显微硬度的影响. 结果表明，上表面粗糙度Sa随上轮廓参数的热输入增加逐渐降低，并在功率为220 W，扫描速度为0.1 m/s时粗糙度Sa达到3.1 μm最优值，但在高热输入时近表面会形成匙孔诱发的孔洞缺陷，因此表面粗糙度优化需折衷考虑近表面孔洞缺陷；此外，双轮廓参数的热输入与下表面粗糙度之间没有明显的相关性.不同轮廓参数下制备的样品下表面粗糙度Sa在13.5 ~ 16.5 μm之间；轮廓参数的单向扫描策略导致了粗大柱状晶粒的形成，并且随着热输入的增加，上层轮廓层的显微硬度显著增加。
Abstract:Although, laser powder bed fusion (LPBF) is widely used in the integrated manufacturing of nickel-based superalloy parts with complex structures in aerospace industry, it’s a relatively rough surface limits its application. In this paper, the surface forming quality was optimized by dual-contours scanning strategy. Meanwhile, the influences of the dual-contours parameters on the surface forming quality, microstructure and microhardness were investigated. The results show that the upper surface roughness Sa gradually decreases with the increase of the heat input of the dual-contours parameters. The roughness Sa reaches the optimal value of 3.1 μm when the power is 220 W and the scanning speed is 0.1 m/s. However, under high heat input, keyhole-induced hole defects will be formed on the near surface. Thus, surface roughness optimization requires comprehensive consideration of the keyhole-induced hole defects. In addition, there is no obvious correlation between the heat input of dual-contours parameters and the roughness of the lower surface. The lower surface roughness Sa of sample prepared under different contour parameters ranges from 13.5 μm to 16.5 μm. The unidirectional scanning strategy of the contour parameter results in the formation of coarse columnar grains, and the microhardness of the upper contour layer increases significantly with the increase of heat input.
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图 1 IN738LC合金粉末
Figure 1. IN738LC powder. (a) distribution of particle size; (b) SEM morphology
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图 2 打印模型
Figure 2. Printing model. (a) model dimension; (b) process parameter definition
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图 4 不同轮廓参数下的表面粗糙度Sa
Figure 4. Surface roughness Sa of sample under different contour parameters. (a) up-surface; (b) down-surface
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图 5 不同轮廓参数的上表面共聚焦形貌
Figure 5. Confocal microscope morphologies of up-surface with different contour parameters. (a) P = 200 W; (b) P = 180 W; (c) P = 140 W
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图 6 不同轮廓参数的下表面共聚焦形貌
Figure 6. Confocal microscope morphologies of down-surface with different contour parameters. (a) P = 200 W; (b) P = 180 W; (c) P = 140 W
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图 7 典型表面形貌特征
Figure 7. Typical morphology of surface. (a) up-surface; (b) down-surface
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图 8 不同扫描速度的上表面侧截面OM形貌(P=180 W)
Figure 8. OM images of dawn-surface cross-section for samples under different scanning speeds (P=180 W). (a) 0.1 m/s; (b) 0.3 m/s; (c) 0.6 m/s
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图 9 不同扫描速度的下表面侧截面OM形貌(P=105 W)
Figure 9. OM images of dawn-surface cross-section for samples under different scanning speeds (P=105 W). (a) 1.2 m/s; (b) 1.6 m/s; (c) 2.0 m/s
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图 10 典型侧截面SEM微观组织
Figure 10. Typical cross-section SEM microstructure. (a) up-surface; (b) in-filling area; (c) down-surface
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图 11 不同扫描速度的显微硬度分布(P=180 W)
Figure 11. Distributions of microhardness for samples under different scanning speeds (P=180 W)
表 1 IN738LC合金粉末的化学成分 (质量分数，%)
Table 1 Chemical compositions of IN738LC powder
含量范围 Cr Co Mo W Ta Al Ti Nb C B Zr Ni 最小值 15.70 8.00 1.50 2.40 1.50 3.20 3.20 0.60 0.09 0.07 0.02 余量 最大值 16.30 9.00 2.00 2.80 2.00 3.70 3.70 1.10 0.13 0.012 0.08 余量 
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