High-Speed Imaging of Laser Powder Bed Fusion Process

This case study examines the formation of defects in manufacturing parts using in situ characterization techniques.

Problem

To understand the mechanisms responsible for the formation of various structure defects in additively manufactured parts, it is essential to develop and apply in situ characterization techniques to monitor the dynamic microstructural evolution in real time. However, due to the highly transient nature of the laser-metal interaction, experimentally characterizing the dynamics of the laser powder bed fusion (LPBF) process has been challenging.

R&D Analysis

Researchers from Carnegie Mellon University, Missouri University of Science and Technology, and Argonne National Laboratory used high-speed X-ray imaging and diffraction techniques at the 32-ID-B beamline at the U.S. Department of Energy’s Advanced Photon Source at Argonne.

Result

The team demonstrated that quantitative structural information on melt pool size/shape, powder ejection, solidification, and phase transformation can be obtained from high resolution, time-resolved x-ray images and diffraction patterns. The experiment platform and the data analysis algorithms they developed will help researchers not only understand the physics underpinning the formation of different defects, but also build high-fidelity models to guide the process optimization for manufacturing parts with different geometries and dimensions.

Benefit of Working with Argonne

High-energy X-ray imaging is needed to see beneath the powder bed and analyze the interactions that affect component quality and performance, including melting and partial vaporization of metallic powders, flow of the molten metals, powder spatter ejection, rapid solidification, and non-equilibrium phase transition. Laser beam interactions with metal occur at the millisecond timescale and produce complex physics reactions. The Advanced Photon Source is the only place in the US with the capability to do high-speed X-ray studies of additive manufacturing (AM) processes. The X-ray data can be fed into physics-based computer models to predict the outcome of changes to AM processing parameters.

More Information

Cang Zhao, Kamel Fezzaa, Ross Cunningham, Haidan Wen, Francesco De Carlo, Lianyi Chen, Anthony Rollett, and Tao Sun, “Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction,” Scientific Reports, 7, 3602 (2017). DOI: 10.1038/s41598-017-03761-2