Multiscale Modeling to Predict Induced Residual Stress, Distortion and Material Properties in Metal Additively Manufactured Components



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This work introduces numerical frameworks that enable the prediction of residual stress (RS), distortion, and microstructure from the metal additive manufacturing (AM) process, to reveal new insights that offer a deeper understanding towards the influence these factors have on RS and distortion induced during subsequent post-process operations. Electron backscatter diffraction (EBSD) imaging reported in the literature has provided evidence of microstructural inhomogeneity in metal AM parts that can strongly influence the resultant anisotropic mechanical response. Unfortunately, EBSD imaging only provides 2D observations of microstructure, and hence assumptions regarding the out-of-plane size and shape of individual grains have to be made. While multiple “slices” of EBSD images can be digitally stitched together, such experimental procedures would be very time-intensive, especially for larger AM builds. Over the past decade, numerous 3D microstructure modeling techniques have emerged and/or evolved to address this difficulty. One such approach involves the Kinetic Monte Carlo (KMC) method. A limitation of the existing KMC method, however, is that its modeling technique only allows for static melt pool and heat affected zone; and it thereby neglects important effects of transient thermal history in metal AM processes. Aside from microstructure, considering the thermomechanical nature of metal AM, rapid thermal cycles can cause large magnitudes of RS and distortion to develop within a fused part during the build. Prior investigations documented in the literature report experimental measurement of tensile residual stresses (TRS) in the bulk AM material, along with several types of surface defects. While TRS can result in poor fatigue life of a component, excessive distortions can lead to part rejection or necessitate expensive and time-consuming post-process correction. It should be noted, however, that the preliminary experimental measurement and characterization of RS via techniques such as slitting, x-ray and/or neutron diffraction are either extremely time consuming, costly, or possess a considerable degree of volumetric averaging. Nonetheless, a poor understanding of the RS fields, distortion, and mechanical response of a metal AM part will adversely influence how the part is post-processed. In addition, the final part geometry may not conform to dimensional requirements or possess the load bearing capacity for the desired application. The foregoing issues motivate the need for a physics-based numerical approach by which the AM microstructure, as well as RS and distortion, can be suitably predicted based on the AM process parameters. Furthermore, such a physics-based model may be of great value in assessing the influence of the initial RS and distortion in the AM part on the subsequent RS and distortion that is induced during post-processing operations such as machining or laser shock peening. In this work, several numerical frameworks are presented and deployed to test hypotheses related to the influences of metal AM RS and inhomogeneous microstructure. First, a Dynamic Kinetic Monte Carlo (DKMC) microstructure prediction framework is developed to capture interlayer and intralayer heat accumulation effects when predicting metal AM microstructure. Unlike the existing KMC approach, the DKMC method captures the influence of the AM process parameter dependent transient thermal history on the printed structure’s grain morphology. This is followed by a study that incorporates the 3D inhomogeneous microstructure for AM metal, predicted via DKMC, in post-process simulations of micromilling as well as laser shock peening (LSP). The work illuminates key insights into how the 3D microstructure consideration influences material response during post-process operations, and it effectively demonstrates a process-structure-property relationship. An investigation into how the initial RS in the bulk AM material influences the post-process induced RS and distortion is subsequently presented with a high-speed machining case study. Furthermore, the extent by which the machining strategy affects the degree of influence of initial RS on the machining-induced RS and distortion is also investigated. The study offers a comprehensive understanding towards the importance of inclusion of initial RS in the AM bulk material when simulating post-process operations. While the aforementioned studies either focus on the effects of initial RS in the AM bulk material or microstructure, they do not combine the two. Hence, an additional study implementing both metal AM microstructure modeling and its initial RS fields is also presented. A parametric examination on the influence of initial RS fields, microstructure, and the printing environment temperature when applying interlayer burnishing during a laser powder bed fusion process reveals new insights regarding their combined effect. Finally, a research application study is presented which demonstrates how numerical prediction of the vertical distortion along the upper surface of the AM build can be used to devise an in-situ LSP strategy to correct for excessive amounts of such surface distortion. While the frameworks presented in this research are implemented using selective laser melting case studies, they are readily extensible to other powder bed fusion metal AM methods, as well as directed energy deposition and binder jetting technologies. New insights from the tools developed in this research facilitate improved understanding through more realistic predictions of residual stress, distortion, and mechanical response of the AM bulk material when subject to post-process treatments.



Metal-work, Additive manufacturing, Laser peening, Laser welding, Lagrangian functions, Residual stresses