Spreading behavior of Ti 48Al 2Cr 2 Nb powders in powder bed fusion additive manufacturing process: experimental and discrete element method study

2021 ◽  
pp. 102489
Author(s):  
Seungkyun Yim ◽  
Huakang Bian ◽  
Kenta Aoyagi ◽  
Kenta Yamanaka ◽  
Akihiko Chiba
Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 392
Author(s):  
Valerio Lampitella ◽  
Marco Trofa ◽  
Antonello Astarita ◽  
Gaetano D’Avino

Laser powder bed fusion additive manufacturing is among the most used industrial processes, allowing for the production of customizable and geometrically complex parts at relatively low cost. Although different aspects of the powder spreading process have been investigated, questions remain on the process repeatability on the actual beam–powder bed interaction. Given the influence of the formed bed on the quality of the final part, understanding the spreading mechanism is crucial for process optimization. In this work, a Discrete Element Method (DEM) model of the spreading process is adopted to investigate the spreading process and underline the physical phenomena occurring. With parameters validated through ad hoc experiments, two spreading velocities, accounting for two different flow regimes, are simulated. The powder distribution in both the accumulation and deposition zone is investigated. Attention is placed on how density, effective layer thickness, and particle size distribution vary throughout the powder bed. The physical mechanism leading to the observed characteristics is discussed, effectively defining the window for the process parameters.


2017 ◽  
Vol 4 (11) ◽  
pp. 11437-11440 ◽  
Author(s):  
Yufan Zhao ◽  
Yuichiro Koizumi ◽  
Kenta Aoyagi ◽  
Kenta Yamanaka ◽  
Akihiko Chiba

Author(s):  
Paul Witherell ◽  
Shaw Feng ◽  
Timothy W. Simpson ◽  
David B. Saint John ◽  
Pan Michaleris ◽  
...  

In this paper, we advocate for a more harmonized approach to model development for additive manufacturing (AM) processes, through classification and metamodeling that will support AM process model composability, reusability, and integration. We review several types of AM process models and use the direct metal powder bed fusion AM process to provide illustrative examples of the proposed classification and metamodel approach. We describe how a coordinated approach can be used to extend modeling capabilities by promoting model composability. As part of future work, a framework is envisioned to realize a more coherent strategy for model development and deployment.


Author(s):  
John C. Steuben ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos

Recent years have seen a sharp increase in the development and usage of Additive Manufacturing (AM) technologies for a broad range of scientific and industrial purposes. The drastic microstructural differences between materials produced via AM and conventional methods has motivated the development of computational tools that model and simulate AM processes in order to facilitate their control for the purpose of optimizing the desired outcomes. This paper discusses recent advances in the continuing development of the Multiphysics Discrete Element Method (MDEM) for the simulation of AM processes. This particle-based method elegantly encapsulates the relevant physics of powder-based AM processes. In particular, the enrichment of the underlying constitutive behaviors to include thermoplasticity is discussed, as are methodologies for modeling the melting and re-solidification of the feedstock materials. Algorithmic improvements that increase computational performance are also discussed. The MDEM is demonstrated to enable the simulation of the additive manufacture of macro-scale components. Concluding remarks are given on the tasks required for the future development of the MDEM, and the topic of experimental validation is also discussed.


2018 ◽  
Vol 73 (3) ◽  
pp. 151-157 ◽  
Author(s):  
Jing Zhang ◽  
Yi Zhang ◽  
Weng Hoh Lee ◽  
Linmin Wu ◽  
Hyun-Hee Choi ◽  
...  

2020 ◽  
Vol 36 ◽  
pp. 101438
Author(s):  
Zachary A. Young ◽  
Qilin Guo ◽  
Niranjan D. Parab ◽  
Cang Zhao ◽  
Minglei Qu ◽  
...  

Author(s):  
Yaqi Zhang ◽  
Vadim Shapiro ◽  
Paul Witherell

Abstract Powder bed fusion (PBF) is a widely used additive manufacturing (AM) technology to produce metallic parts. Understanding the relationships between process parameter settings and the quality of finished parts remains a critical research question. Developing this understating involves an intermediate step: Process parameters, such as laser power and scan speed, influence the ongoing process characteristics, which then affect the final quality of the finished parts. Conventional approaches to addressing those challenges such as powder-based simulations (e.g., discrete element method (DEM)) and voxel-based simulations (e.g., finite element method (FEM)) can provide valuable insight into process physics. Those types of simulations, however, are not well-suited to handle realistic manufacturing plans due to their high computational complexity. Thermal simulations of the PBF process have the potential to implement that intermediate step. Developing accurate thermal simulations, however, is difficult due to the physical and geometric complexities of the manufacturing process. We propose a new, meso-scale, thermal-simulation, which is built on the path-level interactions described by a typical process plan. Since our model is rooted in manufactured geometry, it has the ability to produce scalable, thermal simulations for evaluating realistic process plans. The proof-of-concept simulation result is validated against experimental results in the literature and experimental results from National Institute of Standards and Technology (NIST). In our model, the laser-scan path is discretized into elements, and each element represents the newly melted material. An element-growth mechanism is introduced to simulate the evolution of the melt pool and its thermal characteristics during the manufacturing process. The proposed simulation reduces computational demands by attempting to capture the most important thermal effects developed during the manufacturing process. Those effects include laser-energy absorption, thermal interaction between adjacent elements and elements within the underneath substrate, thermal convection and radiation, and powder melting.


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