The effect of process parameters on residual stress evolution and distortion in the laser powder bed fusion of Ti-6Al-4V

By N. Levkulich, S. Semiatin, Joy E. Gockel, J. Middendorf, A. Dewald, N. Klingbeil. Additive Manufacturing, 2019

193 citations recorded by the source corpus.

Abstract Laser powder bed fusion (LPBF) is a popular additive manufacturing (AM) process that has shown promise in fabricating novel components that can be utilized for a wide variety of applications. However, one of the main drawbacks of LPBF is that it produces large thermal gradients and fast cooling rates during the solidification of each layer, which can lead to large levels of residual stress/distortion, sometimes resulting in build failure/rejection. In the present work, several experimental techniques (x-ray diffraction, hole drilling, contour method, and laser line profilometry) were utilized to establish the effect of LPBF process parameters (scan speed, laser power, build height, build plan area, and substrate condition) on residual stress evolution and distortion. X-ray diffraction and hole-drilling measurements were performed on the surfaces of the LPBF deposits and substrates, while bulk residual stresses were measured using the contour method. In addition, a laser line profilometer was used to measure the distortion after fabrication. The results obtained by the non-destructive and destructive measurement techniques suggested that process parameters greatly influence the development of residual stress and distortion throughout the LPBF deposit and the substrate. Furthermore, the experimental results in this work provide a valuable foundation for future modeling and simulation of the evolution of residual stress and distortion.

Subfields: Automotive Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/3d6f012052f941199a55ea493f7be49c03eac8af

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In Automotive Engineering

1. T. Mukherjee
2. T. DebRoy
3. H. Wei
4. J. Elmer
5. J. Milewski
6. Amitava De
7. Gerald L. Knapp
8. Wei Zhang

In Mechanical Engineering

1. T. Mukherjee
2. T. DebRoy
3. H. Wei
4. J. Elmer
5. J. Milewski
6. Amitava De
7. Gerald L. Knapp
8. Wei Zhang

Citations in the corpus

• Metallurgy, mechanistic models and machine learning in metal printing (2020, background): “107 ) , particularly if the part has other defects such as porosity or lack of fusion defects that may act as sites for stress concentration 108 .”