Advanced Real Time Imaging: Additive Manufacturing
Sponsored by: TMS Functional Materials Division, TMS Structural Materials Division, TMS: Advanced Characterization, Testing, and Simulation Committee, TMS: Alloy Phases Committee, TMS: Biomaterials Committee
Program Organizers: Jinichiro Nakano, MatterGreen; David Alman, National Energy Technology Laboratory; Il Sohn, Yonsei University; Hiroyuki Shibata, Tohoku University; Antoine Allanore, Massachusetts Institute of Technology; Noritaka Saito, Kyushu University; Anna Nakano, US Department of Energy National Energy Technology Laboratory; Zuotai Zhang, Sourthern University of Science and Technology; Candan Tamerler, University of Kansas; Bryan Webler, Carnegie Mellon University; Wangzhong Mu, KTH Royal Institute of Technology; David Veysset, Stanford University; Pranjal Nautiyal , University of Pennsylvania
Wednesday 8:30 AM
March 22, 2023
Room: Aqua 310A
Location: Hilton
Session Chair: Wangzhong Mu, KTH Royal Institute of Technology; Jinichiro Nakano, MatterGreen
8:30 AM Invited
Real Time Imaging of Laser Melting and Re-Solidification: Anthony Rollett1; 1Carnegie Mellon University
The recent, rapid development of ultra-high speed imaging, with diffraction, of the melting, vaporization, resolidification and transformation of metals under laser melting is reprised. Phenomena as diverse as keyhole instability, schlieren imaging of plumes, defect formation, beam steering, phase competition during solidification, hot cracking, residual strain development and solid state transformation are briefly reviewed. The impact on metals additive technology will be discussed. The ways in which such data support computational modeling is reviewed.
8:50 AM Cancelled
High-velocity Interactions of Laser-driven Tin Ejecta Microjets via X-ray Radiography: Yuchen Sun1; J Horwitz1; Kyle Mackay1; S Ali1; J Eggert1; B Morgan1; Fady Najjar1; Hye-Sook Park1; Y Ping1; J Pino1; C Stan1; Alison Saunders1; 1Lawrence Livermore National Laboratory
Ejecta microjets are generated when a shock breaks out from the free surface of a sample and interacts with a surface feature such as a groove or a divot. These jets can travel up to several kilometers per second and be highly destructive and are therefore undesirable in high energy density experiments. Recent studies of laser-driven tin ejecta microjets have demonstrated that two colliding microjets can pass through each other unattenuated or, at higher shock pressures, strongly interact and result in a particle-laden plume. The differences may result from density differences, material phase effects or other complex phenomena. To further investigate, we study the interactions of tin ejecta microjets and additionally the interaction between a microjet and a solid tin substrate through real-time imaging via x-ray radiography to better understand jet interactions. This work will provide new understanding of materials physics driving microjet interaction dynamics. LLNL-ABS-837994. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
9:10 AM
Prediction of Laser Absorptivity from Synchrotron X-ray Images Using Deep Convolutional Neural Networks: Runbo Jiang1; Joseph Aroh1; Brian Simonds2; Tao Sun3; Anthony Rollett1; 1Carnegie Mellon University; 2National Institute of Standards and Technology; 3University of Virginia
The quantification of the amount of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additively manufactured metal components. The geometry of a vapor depression, also known as a keyhole, in melt pools formed during laser melting is closely related to laser absorptivity. This relationship was observed by the state-of-the-art in situ high speed synchrotron x-ray visualization and integrating sphere radiometry. These two techniques create a temporally resolved dataset consisting of keyhole images and the corresponding laser absorptivity. In this work, deep convolutional neural networks including ResNet-50 and ConvNeXt-t are adapted to interpret an unprocessed x-ray image of a keyhole and predict the amount of light absorbed. Class activation map is used for visualizing where deep learning networks pay attention to make predictions. The high-dimensional features extracted is visualized using principal component analysis to identity the relationship between the keyhole geometry and laser absorptivity.
9:30 AM
In-situ Monitoring and Post Operando Analysis of Additively Manufacturing Lunar Regolith Simulants Parts: Caterina Iantaffi1; Chu Lun Alex Leung1; Samy Hocine1; Elena Ruck1; Marta Majkut2; Martina Meisnar3; Thomas Rohr3; Peter D. Lee1; 1University College London; 2European Synchrotron Radiation Facility; 3European Space Agency
Additive Manufacturing (AM) using in-situ resource utilisation (ISRU, i.e. using local resources) has been identified as a key technology for on-demand component fabrication on the moon and extra-terrestrial planets. Laser powder bed fusion (LPBF) has received increasing attention to produce components made of fine lunar grey soil - regolith. On earth, LPBF is a key AM technology; however, obtaining the optimal parameters to AM rich oxidised metals such as regoliths requires extensive characterisation and understanding of the key mechanisms. Here, we use synchrotron x-ray imaging to reveal the laser interaction, melt flow dynamics and solidification mechanism of regoliths. Severe vaporisation and pore formation dominate the process, requiring novel printing strategies to improve densification of two commercial regolith simulants. Both simulants demonstrate different spreadability and laser interactions, studied further on large printed parts to define a processability window. These results help identify practices for additively manufacturing regolith components in space.
9:50 AM
Microstructure Evolution during Laser-based Powder Bed Fusion Studied by Operando X-ray Radiography: Steven Van Petegem1; 1Paul Scherrer Institut
Laser powder bed fusion (L-PBF) is a “layer-by-layer” additive manufacturing process, in which parts are built up by adding precursor powder layers and selectively scanning them with a high-power laser, resulting in the densification of consecutive slices of a three-dimensional object. During L-PBF, heating and cooling rates up to 10 million degrees per second have been reported, leading to far-from-equilibrium microstructures and the formation of unwanted defects such as pores and cracks. To study the evolution of the microstructure during L-PBF, we have developed a dedicated miniaturized L-PBF device optimized for installation at synchrotron X-ray diffraction and imaging beamlines. In this presentation, I will demonstrate how this device was used to study the microstructure evolution in Ti and Fe-based alloys with time resolutions down to 25µs.
10:10 AM Break
10:30 AM
Machine Learning for In-situ Detection of Local Heat Accumulation in Additive Manufacturing: David Guirguis1; Conrad Tucker1; Jack Beuth1; 1Carnegie Mellon University
Metal additive manufacturing is associated with thermal cycles of high rates of heating, melting, cooling, and solidification. Some areas within the build experience thermal cycles depending on the paths of the energy source. In addition, geometrical features, such as thin walls and overhangs, can lead to heat accumulation which affects the microstructure, fatigue life, and induced residual stresses that may lead to dimensional distortion and cracking. Identification of significant heat accumulation can be used for part quality monitoring, to inform the design process to enhance the quality of printed parts, and to optimize the process parameters. In the present work, we use in-situ thermal monitoring of builds by IR imaging. A computational framework, employing unsupervised machine learning, is developed to detect zones of heat accumulation in the CAD geometry. The effectiveness of this framework is demonstrated by implementation on builds with different geometrical features.
10:50 AM
Mapping the Melt Pool Variability in L-PBF Additive Manufacturing by High-Speed Imaging: David Guirguis1; Conrad Tucker1; Jack Beuth1; 1Carnegie Mellon University
Laser powder bed fusion (L-PBF) is a well-established technology for additive manufacturing of metal alloys. However, the uncertainty and variability in the quality of printed parts are still of major concern. Insights into the variability of melt pool dimensions are crucial to determine process parameters for microstructural control and enhancement of mechanical properties. Additionally, significant variability can lead to flaws. In this work, we analyze and quantify the variation in melt pool attributes by utilization of high-speed imaging at a frame rate of up to 216,000 frames per second. Quantification and mapping of the melt pool variation are performed with different process parameters for Ti-6Al-4V and ultra-high-strength steel alloy AF–9628. The results of this study can help better understand the correlation between the dimensional variability and the process parameters. In addition, computational models for optimum hatch spacing are developed employing Bayesian machine learning.