In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.
This content will become publicly available on May 1, 2025
Laser-directed energy deposition (DED), a metal additive manufacturing method, is renowned for its role in repairing parts, particularly when replacement costs are prohibitive. Ensuring that repaired parts avoid residual stresses and deformation is crucial for maintaining functional integrity. This study conducts experimental and numerical analyses on trapezoidal shape repairs, validating both the thermal and mechanical models with experimental results. Additionally, the study presents a methodology for creating a toolpath applicable to both the DED process and Abaqus CAE software. The findings indicate that employing a pre-heating strategy can reduce residual stresses by over 70% compared to no pre-heating. However, pre-heating may not substantially reduce final distortion. Notably, final distortion can be significantly mitigated by pre-heating and subsequently cooling to higher temperatures, thereby reducing the cooling rate. These insights contribute to optimizing DED repair processes for enhanced part functionality and longevity.
more » « less- Award ID(s):
- 1937128
- PAR ID:
- 10540218
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Materials
- Volume:
- 17
- Issue:
- 10
- ISSN:
- 1996-1944
- Page Range / eLocation ID:
- 2179
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Additively-manufactured parts contain residual stresses induced by manufacturing. These residual stresses can be relaxed or redistributed by thermal loading. The presence of internal stress influences the dynamic response of parts, and this is of particular interest in thin plates subject to thermoacoustic loading in hypersonic vehicles and fusion reactors.
Objective To measure the changes in shape and modal frequencies caused by thermal loading of geometrically-reinforced thin plates that were additively manufactured in Inconel 625.
Methods Plates were additively-manufactured in landscape and portrait orientations using laser powder bed fusion. The plates were heated to a nominal temperature of 820 ̊C, which was expected to alleviate the residual stress from the build process. Pre- and post-heating, their modal frequencies were found experimentally and pulsed-laser stereo (3D) digital image correlation was used to evaluate their modal shapes. The resultant modal frequencies and shapes were compared with those from a subtractively-manufactured plate.
Results It was found that the heat cycle changed the shape of the plates relative to their as-manufactured state in addition to changing their natural frequencies and modal shapes.
Conclusions The change in shape induced by heating caused shifts in the natural frequencies and changes in the corresponding modal shapes. The results show quantitatively for the first time the important role that residual stresses can play in the dynamic response of geometrically-reinforced thin plates manufactured by additive and subtractive processes.
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M. Wiercigroch, editof-in-chief (Ed.)Additive manufacturing (AM) is known to generate large magnitudes of residual stresses (RS) within builds due to steep and localized thermal gradients. In the current state of commercial AM technology, manufacturers generally perform heat treatments in effort to reduce the generated RS and its detrimental effects on part distortion and in-service failure. Computational models that effectively simulate the deposition process can provide valuable insights to improve RS distributions. Accordingly, it is common to employ Computational fluid dynamics (CFD) models or finite element (FE) models. While CFD can predict geometric and thermal fluid behavior, it cannot predict the structural response (e.g., stress–strain) behavior. On the other hand, an FE model can predict mechanical behavior, but it lacks the ability to predict geometric and fluid behavior. Thus, an effectively integrated thermofluidic–thermomechanical modeling framework that exploits the benefits of both techniques while avoiding their respective limitations can offer valuable predictive capability for AM processes. In contrast to previously published efforts, the work herein describes a one-way coupled CFDFEA framework that abandons major simplifying assumptions, such as geometric steady-state conditions, the absence of material plasticity, and the lack of detailed RS evolution/accumulation during deposition, as well as insufficient validation of results. The presented framework is demonstrated for a directed energy deposition (DED) process, and experiments are performed to validate the predicted geometry and RS profile. Both single- and double-layer stainless steel 316L builds are considered. Geometric data is acquired via 3D optical surface scans and X-ray micro-computed tomography, and residual stress is measured using neutron diffraction (ND). Comparisons between the simulations and measurements reveal that the described CFD-FEA framework is effective in capturing the coupled thermomechanical and thermofluidic behaviors of the DED process. The methodology presented is extensible to other metal AM processes, including power bed fusion and wire-feed-based AM.more » « less
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Purpose The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process. Design/methodology/approach In this study, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples. Findings The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error. Research limitations/implications This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts. Practical implications The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation. Originality/value This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.more » « less
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The objective of this paper is to experimentally validate the graph-based approach that was advanced in our previous work for predicting the heat flux in metal additive manufactured parts. We realize this objective in the specific context of the directed energy deposition (DED) additive manufacturing process. Accordingly, titanium alloy (Ti6Al4V) test parts (cubes) measuring 12.7 mm × 12.7 mm × 12.7 mm were deposited using an Optomec hybrid DED system at the University of Nebraska-Lincoln (UNL). A total of six test parts were manufactured under varying process settings of laser power, material flow rate, layer thickness, scan velocity, and dwell time between layers. During the build, the temperature profiles for these test parts were acquired using a single thermocouple affixed to the substrate (also Ti6Al4V). The graph-based approach was tailored to mimic the experimental DED process conditions. The results indicate that the temperature trends predicted from the graph theoretic approach closely match the experimental data; the mean absolute percentage error between the experimental and predicted temperature trends were in the range of 6% ∼ 15%. This work thus lays the foundation for predicting distortion and the microstructure evolved in metal additive manufactured parts as a function of the heat flux. In our forthcoming research we will focus on validating the model in the context of the laser powder bed fusion process.more » « less
