Additive Manufacturing: Advanced Characterization with Synchrotron, Neutron, and In Situ Laboratory-scale Techniques II: In Situ Monitoring of Laser Powder-bed Fusion
Sponsored by: TMS Structural Materials Division, TMS: Additive Manufacturing Committee, TMS: Advanced Characterization, Testing, and Simulation Committee
Program Organizers: Fan Zhang, National Institute of Standards and Technology; Donald Brown, Los Alamos National Laboratory; Andrew Chuang, Argonne National Laboratory; Joy Gockel, Colorado School of Mines; Sneha Prabha Narra, Carnegie Mellon University; Tao Sun, University of Virginia

Tuesday 2:30 PM
March 1, 2022
Room: 258A
Location: Anaheim Convention Center

Session Chair: Joy Gockel, Colorado School of Mines

2:30 PM  Invited
NOW ON-DEMAND ONLY - Digitally Twinned Additive Manufacturing: Real-time Detection of Flaws in Laser Powder Bed Fusion by Combining Thermal Simulations with In-Situ Meltpool Sensor Data.: Reza Yavari1; Alex Riensche1; Emine Tekerek2; Adriane Tenequer3; Lars Jacquemetton4; Scott Halliday3; Marcie Vandever3; Ziyad Smoqi1; Vignesh Perumal2; Kevin Cole1; Antonios Kontsos2; Prahalad Rao1; 1University of Nebraska; 2Drexel University; 3Navajo Technical University; 4Sigma Labs
    The objective of this work is to develop and apply a physics and data integrated strategy to detect incipient flaw formation in laser powder bed fusion (LPBF) parts. The approach used to realize this objective is based on combining (twinning) real-time in-situ meltpool temperature measurements with a graph theory-based thermal simulation model that rapidly predicts the part-level temperature distribution in the part (thermal history). The novelty of the approach is that the temperature distribution predictions provided by the computational thermal model are updated with real-time meltpool temperature measurements. This digital twin approach is applied to detect flaw formation in stainless steel (316L) impeller-shaped parts made using a commercial LPBF system.

3:00 PM  
Simultaneous 3D-location and Temperature Tracking of Hot Spatter during Laser Powder Bed Fusion Using a High-speed Spectral Plenoptic Camera: Ralf Fischer1; Dustin Kelly1; Sarah Morris1; Ari Goldman1; Brian Thurow1; Barton Prorok1; 1Auburn University
    Unlike conventional light-field cameras, plenoptic cameras can capture 3D information of a scene instantaneously with just a single sensor. In combination with a high-speed camera, this feature enables 3D-particle tracking with minimal setup and space requirements compared to traditional four camera tomographic designs. The plenoptic camera is composed of a traditional camera sensor, main lens, and a microlens array where each microlens images the aperture plane of the main lens. Locating different spectral filters at the aperture plane provides the unique ability for each microlens to capture spectral information. This work utilizes these unique features to track spatter particles ejected from the Laser-Powder Bed Fusion process to conduct particle tracking velocimetry, using plenoptic ray bundling, with simultaneous temperature measurements, using dual-wavelength pyrometry, for various processing parameters to gain more insight into the fundamentals of the L-PBF process.

3:20 PM  Cancelled
Process Monitoring of Melt Pool Spatter at Melt Pool, Layer and Part Scales: Jack Beuth1; Christian Gobert1; Brandon Abranovic1; 1Carnegie Mellon University
    Emission of melt pool spatter is a significant concern for laser powder bed fusion AM. When spatter particles land on unfused material, large lack of fusion-type defects can result. This research involves in-process monitoring of spatter generation by machine learning analyses of high speed videos at the melt pool scale, infrared videos of entire builds, and individual images of fused layers. The combination of these is allowing determination of spatter counts and trajectories as spatter is emitted from melt pools, tracking of spatter particles as they are captured by the flow of argon across the build, and identification of spatter after it has landed on powder. This talk will present results showing changes in spatter counts and trajectories as a function of process parameters, the ability of argon flows to direct spatter away from unfused regions, and the correlation of spatter seen in fused layers with build conditions.

3:50 PM Break

4:05 PM  
Sensor Enabled Material Response Prediction in Powder Bed Fusion Additive Manufacturing: Justin Gambone1; 1GE Research
    PBFAM is becoming an increasingly utilized manufacturing technique for multiple industrial applications and with this machine consistency and robustness is an essential capability. Current systems use a variety of sensors to monitor the additive process throughout a build, but do not draw direct correlations to resulting material and part behavior. Through the use of robust experimental and sensor datasets, machine learning techniques can be applied to better associate in-process behavior to final material quality. The focus of this work is to leverage on-machine sensors, tied to post-build quality information regarding the meltpool and part, to identify system performance and resulting part quality. The results of which are used to further improve the understanding of the build process and provide input for improved local control of the additive system.

4:25 PM  
In Situ Characterization of Laser-based Metal Additive Manufacturing by Detection of Thermal Electron Emission: Philip Depond1; John Fuller1; Saad Khairallah1; Justin Angus1; Gabe Guss1; Manyalibo Matthews1; Aiden Martin1; 1Lawrence Livermore National Laboratory
    Current diagnostic methods for laser powder bed fusion additive manufacturing (AM) capture optical images, X-ray radiographs, or measure the emission of thermal or acoustic signals from the component. Here we discuss a methodology based on the thermal emission of electrons - thermionic emission - from the metal surface during laser heating. Experimental studies show increases in thermionic emission are correlated to laser scanning conditions that give rise to pore formation and regions where surface defects are pronounced. State of the art multi-physics simulations corroborate the relationship between increased melt depression depth and thermionic signal including perturbations in the melt pool that lead to material ejection (i.e., spatter). The information presented here is a critical step in furthering our understanding and validation of laser-based metal AM and demonstrates that the collected thermionic signals can be incorporated into conventional data collection schemes and processing methods.

4:45 PM  
In-situ Characterization of Melt Flow Instability in Laser Metal Additive Manufacturing: Qilin Guo1; Minglei Qu1; S. Mohammad H. Hojjatzadeh1; Luis I. Escano1; Zachary Young1; Kamel Fezzaa2; Lianyi Chen1; 1University of Wisconsin Madison; 2Argonne National Laboratory
    Melt flow plays a critical role in laser melt additive manufacturing, controlling the melt pool development, defect formation/evolution, solidification, and spatter generation. However, during common process designing or modeling, the melt flow is always assumed to maintain a “regular” pattern within a melt pool, without considering the unstable flow behavior. Here, we report the melt flow instability in laser metal additive manufacturing process. By in-situ high-speed high-energy synchrotron X-ray imaging, for the first time, we will reveal the mechanisms for causing the instabilities of melt flow, as well as the consequences brought by the unstable flow in additive manufacturing process. The results provide crucial insights into laser additive manufacturing processes.