journal article·2021-10-29Open access

Effect of Process Parameters and Layer Thickness on the Quality and Performance of Ti-6Al-4V Fabricated by Selective Laser Melting

Yanlong Jing, Peng Wang, Xiaoling Yan

Coatings· Volume 11· Issue 11

Abstract

To improve the quality of thick powder bed and realize the matching of thick powder bed and thin powder bed in the later stage, the influence of process parameters for the single-track, multi-layer fabrication, relative density, surface quality, defect, remelting, and boundary optimization performance of different layer thicknesses of Ti-6Al-4V fabricated by selective laser melting were investigated. It is more conducive to the stable forming of single-track when the point distance is half the diameter of the laser beam, and the exposure time is appropriately extended. The thin powder bed needs the corresponding point distance and exposure time under the laser power of 280–380 W to obtain high-density specimens. The thick powder bed needs to be able to ensure the formation of high-quality specimens under the smaller point distance and longer exposure time under higher laser power of 380 W. Both thick powder bed and thin powder bed will cause un-melted defects between molten pools, spheroidization defects caused by splashing, and microporous defects. The remelting process can significantly improve the surface quality of the formed specimen, but the surface quality of the thick powder bed is worse than that of the thin powder bed. The boundary quality of thick powder bed is worse than that of thin powder bed, and the boundary shape has a greater influence on the quality of the SLM forming boundary. Different strategies should be adopted to form the boundary of different shapes. Increasing the boundary count and increasing the laser power are more conducive to the improvement of boundary quality.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Welding Techniques and Residual Stresses

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China

Funding

National Natural Science Foundation of China

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