Additive Manufacturing: Advanced Characterization for Industrial Applications: In-situ and Operando Techniques
Sponsored by: TMS Advanced Characterization, Testing, and Simulation Committee, TMS Additive Manufacturing Bridge Committee
Program Organizers: Nadia Kouraytem, Utah State University; Fan Zhang, National Institute of Standards and Technology; Lianyi Chen, University of Wisconsin-Madison
Monday 8:00 AM
October 18, 2021
Room: A121
Location: Greater Columbus Convention Center
Session Chair: Nadia Kouraytem, Utah State University
8:00 AM Introductory Comments
8:10 AM
Characterizing Powder Spreading Dynamics in Powder Bed Fusion AM Process by High-speed X-ray Imaging: Luis Escano1; Lianyi Chen1; 1University of Wisconsin - Madison
Powder flowing behavior during the spreading process in powder-bed-based additive manufacturing technologies will ultimately define the powder bed quality. However, the dynamics of powders under additive manufacturing conditions is still not fully understood. It is important to experimentally observe the powder moving dynamics during the spreading process to understand the detailed mechanisms. However, the in-situ characterization of such process is quite a challenge due to the high spreading speed, the microscale of the particles, and the opacity of the material. Here we present our novel methodology for in-situ characterization of powder spreading dynamics in powder-based AM processes by high-speed x-ray imaging.
8:30 AM
Domain Adaption for Enhanced X-ray CT Reconstruction of Metal Additively Manufactured Parts: Amir Ziabari1; Abhishek Dubey2; Singanallur Venkatakrishnan1; Michael Sprayberry1; Curtis Frederick3; Paul Brackman3; Philip Bingham1; Ryan Dehoff1; Vincent Paquit1; 1Oak Ridge National Laboratory; 2NIH; 3Carl Zeiss Industrial Metrology, LLC
Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE of AM parts. However, standard reconstruction algorithms may face challenges when processing XCT of metal AM parts due to phenomenon such as noise, streaks/metal artifacts and beam hardening. In this work, we present our deep learning-based CT reconstruction method, which we call SIMURGH, that leverages computer-aided design (CAD) models of the AM parts along with accurate XCT simulations, CycleGAN domain adaptation and a residual CNN to remove metal artifacts and beam hardening from the reconstructed 3D volumes. Promising results both on synthetic and real data sets are shown, demonstrating significant improvement in defect detection capability in XCT of AM parts, which is confirmed by multi-scale high resolution XCT reconstructions as the ground truth.
8:50 AM
In-situ Quality Monitoring of PBF AM Parts: Bernard Revaz1; 1SENSIMA Inspection/AMIquam SA
We develop a compliant, eddy current based solution to monitor in-situ the quality of metal parts manufactured by powder bed fusion. Our solution differs from usual monitoring approaches because it is sensitive to subsurface layers and flaws that will remain in the final part and not the surface features that are re-melted or to the melt pool properties that are unfortunately indirectly linked to the part quality. We present integration projects on commercial machines that have been instrumented with minimal interfaces using our autonomous, wireless detection unit. Data collected during fabrications with most of the materials used in LPBF will be discussed and the relationship with the porosity will be presented. The solutions can also be applied to inspect lattice like structures, whose in-situ and post build inspection remains a challenge. A remote NDT monitoring architecture, enabling third parties from the control and inspection industry to certify the parts remotely, is introduced.
9:10 AM
Understanding the Keyhole Dynamics in Laser Welding Using Time-resolved X-ray Imaging Coupled with Computer Vision and Data Analytics: Joseph Aroh1; Jongchan Pyeon1; Runbo Jiang1; Benjamin Gould2; Andy Ramlatchan3; Anthony Rollett1; 1Carnegie Mellon University; 2Argonne National Laboratory; 3NASA Langley Research Center
During laser welding of metals, localized metal evaporation resulting in the formation of a keyhole shaped cavity can occur if high enough energy densities are used. An unstable keyhole can have deleterious effects in certain applications (e.g. laser powder bed fusion) as it increases the likelihood of producing defects such as spatter or porosity. In this work, the dynamics of keyhole fluctuations were probed using in-situ synchrotron x-ray imaging at the Advanced Photon Source across a range of materials and laser parameters. The high temporal and spatial resolution of these experiments result in large datasets which were processed using computer vision techniques in order to extract time-resolved quantitative geometric features. These features were analyzed and a correlation was made between local keyhole geometry variation and the presence/absence of processing defects. Likewise, multivariate statistical tools were employed to understand the relationship between processing parameters, material properties, and keyhole geometry.