2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024): Applications: Residual Stress
Program Organizers: Joseph Beaman, University of Texas at Austin
Monday 1:30 PM
August 12, 2024
Room: 416 AB
Location: Hilton Austin
Session Chair: Frank Liou, Missouri University of Science and Technology
1:30 PM
A Three-dimensional Semi-analytical Thermo-elastic-plastic Model for Real-time In-situ Prediction of Residual Stress in Laser Powder Bed Fusion: Tao Liu1; Edward Kinzel2; Ming Leu1; 1Missouri University of Science and Technology; 2University of Notre Dame
Residual stresses caused by non-uniform heating in parts produced by laser powder bed fusion (LPBF) can lead to geometric distortion and mechanical defects. There is a significant interest in in-situ, real-time predictions of residual stress to optimize printing parameters and enhance feedback control of printing performance. This paper introduces Green's function solutions for simulating three-dimensional (3D) temperature and residual stress fields in LPBF processes. Leveraging a semi-infinite domain, volumetric Gaussian laser profiles, and GPU acceleration, the semi-analytical model achieves high computational efficiency. The model predictions of temperature and residual stress under various scanning strategies are validated by the Finite Element Method (FEM). Furthermore, this 3D semi-analytical model is utilized to predict cantilever beam deformations resulting from different scanning strategies. The predictions align well with experimental results, therefore facilitating the selection of process parameters that minimize residual stress and the proactive detection and correction of defects.
1:50 PM
In-Situ Laser Powder Bed Fusion: Real-Time Assessment of Residual Stress through Thermal Gradient Analysis: Xinyi Xiao1; Hanbin Xiao1; 1University of North Texas
Metal additive manufacturing processes have garnered significant attention due to their ability to enhance design flexibility and manufacturability. However, the rapid heating and cooling inherent in these processes often lead to deviations in the as-built properties. Residual stress emerges as a critical factor contributing to these deviations, posing a risk of build failure and adversely affecting the functionality of the fabricated parts. To address these challenges, this study proposes a data-efficient computational machine learning model integrated with process-related physical phenomena. By establishing a quantitative link between observed in-situ phenomena and residual stress, the framework facilitates a deeper understanding of the metal AM process. The developed model not only enhances the ease-of-use but also converts real-time monitoring data into prognostic signals, providing insights into the metal AM process's quality. This approach is instrumental in predicting and controlling the as-built residual stress, ultimately improving the overall quality of fabricated metal AM parts.
2:10 PM
Mitigating Distortion Challenges in Additively Manufactured Metallic Parts: Maria Strantza1; Alex Reikher1; Gabe Guss1; Steven Hoover1; David Macknelly2; 1Lawrence Livermore National Laboratory; 2 Atomic Weapons Establishment
The additive manufacturing process generates inherent thermal history, frequently causing unwanted and occasionally detrimental residual stresses. These stresses can manifest as distortion, cracking, and delamination. As the desire for larger metallic structures with finer geometric tolerances grows, the need to understand, and mitigate distortions induced by this process has become imperative. In this investigation, we employ a combination of experimental and simulation approaches for distortion compensation, aimed to achieve better part quality and dimensional stability on simple and complex metal geometries. Prepared by LLNL under Contract DE-AC52-07NA27344.
2:30 PM
Ballistic Performance Predictions of As-built Additively Manufactured Stainless Steel: David Failla1; Jean Santiago-Padilla2; Matthew Priddy1; 1Mississippi State University; 2U.S. Army Engineer Research and Development Center
Residual stress predictions can help with the development of as-built additively manufactured (AM) parts for collision applications by highlighting stress localizations due to thermal cycling, determining areas of premature failure, and part manufacturability. The current work leverages a sequentially coupled thermomechanical finite element (FE) framework to predict the effects of residual stresses on the ballistic performance of as-built AM 316L stainless steel square coupons. A moving heat source and progressive element activation scheme, mimicking the laser-powder bed fusion (L-PBF) process, dictate the nodal thermal history of the plate. Sequentially, a mechanical analysis predicts the residual stress state by leveraging the output thermal history. Finally, the ballistic impact FE simulation leverages the calculated residual stress state of the as-built component to predict its effect on the residual velocity of the penetrator and energy absorption of the target. The final ballistic predictions are compared to experimental results to examine model accuracy.
2:50 PM
Exploring the Impact of Pre- and Post-heating on Residual Stresses in Laser-directed Energy Deposition: A Numerical Investigation: Usman Tariq1; Sung-Heng Wu1; Muhammad Arif Mahmood1; Frank Liou1; 1Missouri University of Science and Technology
Laser Directed Energy Deposition (DED) is a technique that utilizes a laser to create a melt pool and deposit layers of material by pouring powder into it. During the DED process, the laser induces local heating, leading to localized deformation and residual stresses due to temperature variations. These factors can significantly contribute to premature failure of in-service parts. Based on the need, this study aims to explore pre- and post-heating techniques during the DED process, aimed at mitigating the temperature differential before and after deposition. Additionally, the study investigates various pre-heating and post-heating temperatures to determine the most suitable scenario for reducing residual stresses within the final part.
3:10 PM Break
3:40 PM
A Fast Data-driven Residual Stress Prediction for Laser Powder Bed Fusion Additive Manufacturing Based on the Modified Inherent Strain Method: Praveen Vulimiri1; Shane Riley1; Florian Dugast1; Albert To1; 1University of Pittsburgh
Metal additive manufacturing processes, such as laser powder bed fusion or directed energy deposition, melt and fuse material to an existing structure to build a part sequentially. The repeated heating and cooling cycles introduce thermal stress, which can cause the part to distort and crack. While simulation can help predict the stress, the computational time required could potentially take longer than manufacturing the part. In this work, a data-driven, geometry agnostic mean-variance estimation model was developed to predict the residual stress in a few seconds. The model was trained on over 800 geometries from the Princeton University ModelNet database, simulated using the layerwise inherent strain method. For unseen parts, the normalized error of the predicted stress is around 10% on average, and 95% of errors are within two standard deviations of the predicted variance at each element.
4:00 PM
Laser Speckle Photometry for Residual Stress Diagnosis of Laser Powder Bed Fusion Processing: Justin Krantz1; Gonzalo Reyes-Donoso1; Robert Landers1; Cody Lough2; Ben Brown2; Edward Kinzel1; 1University of Notre Dame; 2Kansas City National Security Campus
A major challenge in Laser Powder Bed Fusion (LPBF) process development is the presence of residual stresses in the as-built material. Detection and reduction of these residual stresses would improve the quality of LPBF parts. Laser speckle photometry involves illumination of the build area with a visible laser, creating a speckle pattern. Residual stress driven deformation in the material can be observed at resolutions below the diffraction limit by observing changes in the laser speckle pattern. A femtosecond laser ablates the material to release residual stress. The resulting strain can then be observed. This allows for the measurement of residual stresses in-situ while simultaneously providing the ability to release these stresses to improve material properties. The Department of Energy’s Kansas City National Security Campus (operated and managed by Honeywell Federal Manufacturing Technologies, LLC under contract number DE-NA0002839) funded this work.
4:20 PM
Influencing Residual Stress in PBF-LB/M Processes through In-Process Microstructure Assessment and Selective Laser Heat Treatment: Jork Groenewold1; Julian Hammes1; Florian Stamer1; Gisela Lanza1; 1wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT)
In powder bed fusion by laser beam melting of metal (PBF-LB/M), residual stresses form in the fabricated layers and affect the process by promoting deformations and cracks. Certain tool steels are especially prone to such defects. This paper presents a concept to reduce residual stresses in the PBF-LB/M manufacturing process of H13 tool steel through selective laser heat treatment, inducing solid-state phase transformations. These directly influence residual stresses due to the associated volume change of the crystal lattice. To assess the microstructure, an eddy current sensor is attached to the coating unit. Local temperature conditions are recorded using a high-speed thermographic camera. The captured data is translated into models using Bayesian optimization and gaussian process regression. Subsequently, these measurements and models are utilized determine process parameters for local laser heat treatment. The targeted outcome is a technology that reduces residual stresses, thereby minimizing deformations and cracks in additively manufactured components.