Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales

Abstract

Powder bed fusion additive manufacturing (PBFAM) of metals has the potential to enable new paradigms of product design, manufacturing and supply chains while accelerating the realization of new technologies in the medical, aerospace, and other industries. Currently, wider adoption of PBFAM is held back by difficulty in part qualification, high production costs and low production rates, as extensive process tuning, post-processing, and inspection are required before a final part can be produced and deployed. Physics-based modeling and predictive simulation of PBFAM offers the potential to advance fundamental understanding of physical mechanisms that initiate process instabilities and cause defects. In turn, these insights can help link process and feedstock parameters with resulting part and material properties, thereby predicting optimal processing conditions and inspiring the development of improved processing hardware, strategies and materials. This work presents recent developments of our research team in the modeling of metal PBFAM processes spanning length scales, namely mesoscale powder modeling, mesoscale melt pool modeling, macroscale thermo-solid-mechanical modeling and microstructure modeling.Ongoing work in experimental validation of these models is also summarized. In conclusion, we discuss the interplay of these individual submodels within an integrated overall modeling approach, along with future research directions. K E Y W O R D Sadditive manufacturing, defects, melt pool, microstructure, modeling and simulation, powder, powder bed fusion, residual stressesThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A simple yet consistent constitutive law and mortar-based layer coupling schemes for thermomechanical macroscale simulations of metal additive manufacturing processes

“…Especially the rapid cross section changes of a rather complex geometry, involving a hollow pyramid as well as a solid pyramid with curved surfaces, illustrated the effectiveness of the proposed mortar mesh tying strategy. new strain contribution ε − ε T for the remolten fraction, ε 4 ref = 0.5ε 3 ref + 0.5(ε − ε T ) = 0.75(ε − ε T ). The same logic applies to the next heating at t 5 and cooling t 6 and further heating-cooling cycles.…”

“…Existing modeling approaches for PBFAM are often distinguished by the modeled length scales (see overview in [3,4]). Macroscale or part-scale models determine residual stresses or dimensional warping.…”

Thermo-Mechanical Coupling Analysis of the Sealing Structure Stress of LNG Cryogenic Hose Fittings

“…This includes determining the various stress components of the weld, such as axial tension, transverse shear, normal stress, etc. [20][21][22]. The average stress at the weld needs to be lower than the allowable stress of the weld metal in order to prevent the strength of the weld from being compromised, that is…”

Predicting geometric errors and failures in additive manufacturing

“…Quality assurance can be achieved by using supervised learning methods to predict the quality of a manufactured item. Decker et al (2020) analytically present research on shape deformation through predictive modeling and compensation approaches, dividing them into two main categories of predictive modeling approaches: physics-based approaches that use finite element modeling (Meier et al , 2021) and data-driven approaches based on statistical analysis and machine learning (Zhu et al , 2018). In the scope of this study, we focus on statistical analysis and machine learning approaches owing to their more generalized outlook.…”