Additive Manufacturing Benchmarks 2022 (AM-Bench 2022): In-Process Measurements and Models
Program Organizers: Brandon Lane, National Institute of Standards and Technology; Lyle Levine, National Institute of Standards and Technology
Monday 1:30 PM
August 15, 2022
Room: Old Georgetown Room
Location: Hyatt Regency Bethesda
Session Chair: Ho Yeung, National Institute of Standards and Technology
1:30 PM Invited
In-situ Monitoring of the Laser Powder Bed Fusion Process by Thermography, Optical Tomography, Melt Pool Monitoring and Eddy Current Testing for Defect Detection: Nils Scheuschner1; Frank Heinrichsdorf2; Anzhelika Gordei3; Jaroslaw Kochan3; Henrik Ehlers1; Hamid Jahangir4; Matthias Pelkner1; Toni Räpke1; Christiane Maierhofer1; Kai Hilgenberg1; 1Bundesanstalt für Materialforschung und –prüfung (BAM); 2Siemens AG; 3Fraunhofer IPK; 4Siemens Energy Global GmbH & Co. KG
Since AM is prone to the formation of defects during the building process, a fundamental requirement for AM is to find ways to assure the safety and reliability of the additively manufactured parts to become applicable in most fields. A possible solution for this problem lies in the deployment of various in-situ monitoring techniques. In this contribution, we present results of a study of L-PBF printed parts made of the superalloy Haynes 282, in which the formation of defects was provoked by local variations of the process parameters such as the laser power. These parts were monitored in-situ by thermography, optical tomography, melt pool monitoring and eddy current testing and ex-situ by computed tomography (CT) with the goal to evaluate the feasibility and prospect of the individual methods for the reliable detection of the formation of relevant defects.
2:00 PM
Improving Part Quality in Laser Powder Bed Fusion using Model-based Feedforward Control of Thermal History: Alexander Riensche1; Benjamin Bevans1; Ziyad Smoqi1; Reza Yavari2; Prahalad Rao1; Ajay Krishnan3; 1University of Nebraska; 2Vulcan Forms; 3Edison Welding Institute
In this work we improved the laser powder bed fusion part (e.g., reducing microstructure, geometrical accuracy, distortion, and swelling) solely by modifying the thermal history of components being built. We demonstrated the viability of this approach through two builds with one acting as a control and the other with thermal history modification. The thermal history modifications were executed in a feed-forward manner, by leveraging the results of a fast graph theory-based thermal modeling approach and real-time IR camera data to modify process parameters.
2:20 PM
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; Prahalad Rao1; Alex Riensche1; Emine Tekerek2; Lars Jacquemetton3; Ziyad Smoqi1; Vignesh Perumal2; Antonios Kontsos2; Harold (Scott) Halliday4; Kevin Cole1; 1University of Nebraska; 2Drexel University; 3Sigma Labs; 4Navajo Technical University
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. This digital twin approach is applied to detect flaw formation in stainless steel (316L) impeller-shaped parts. Four such impellers are produced emulating three pathways of flaw formation in LPBF parts, these are: changes in the processing parameters (process drifts); machine-related malfunctions, and deliberate tampering with the process to plant flaws inside the part (cyber intrusions). The digital twin approach is shown to be effective for detection of the three types of flaw formation causes studied in this work.
2:40 PM
Online Prediction of Porosity in Laser Powder Bed Fusion using Physics-informed Meltpool Signatures and Machine Learning: Prahalad Rao1; Ziyad Smoqi1; Aniruddha Gaikwad1; Benjamin Bevans1; Md Humaun Kobir1; James Craig2; Alan Abul-Haj3; Alonso Peralta4; 1University of Nebraska; 2Stratonics; 3ARA Engineering; 4Honeywell
In this work we accomplished the online prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally light machine learning approaches. Q large cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. Physically relevant signatures, such as meltpool length, temperature, and ejecta characteristics, were extracted from the meltpool images. Relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95%.
3:00 PM Break