Powder-Scale Meshfree Simulations of Powder Bed Fusion Based Additive Manufacturing Processes

2019 ◽  
Author(s):  
Zongyue Fan ◽  
Hao Wang ◽  
Bo Li
Keyword(s):  
Additive Manufacturing ◽  
Optimal Transportation ◽  
The Novel ◽  
Powder Bed Fusion ◽  
Computational Framework ◽  
Powder Bed ◽  
Materials Behaviors ◽  
Melt Pool ◽  
Powder Particles ◽  
Am Processes

Abstract We present a powder-scale meshfree direct numerical simulation (DNS) capability for the powder bed fusion (PBF) based additive manufacturing (AM) processes using the novel Hot Optimal Transportation Meshfree (HOTM) method. The HOTM method is an incremental Lagrangian meshfree computational framework for materials behaviors under extreme thermomechanical loading conditions, which combines the Optimal Transportation Meshfree (OTM) method and the variational thermomechanical constitutive updates. The realistic multi-layer powder bed geometry is modeled explicitly in the HOTM simulations based on experimental data. A phase-aware constitutive model is developed to predict the phase change and multiphase mixing during the PBF AM processes automatically. The governing equations including the linear momentum and energy conservation equations are solved for the multiphase flow simultaneously to predict the deformation, temperature and local state of the powder particles. The powder-scale DNS is employed to study the influence of various laser powers on the melt pool thermodynamics.

scholarly journals An Overview of Additive Manufacturing Technologies—A Review to Technical Synthesis in Numerical Study of Selective Laser Melting

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3895 ◽  
Author(s):  
Abbas Razavykia ◽  
Eugenio Brusa ◽  
Cristiana Delprete ◽  
Reza Yavari
Keyword(s):  
Numerical Simulation ◽  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Powder Bed Fusion ◽  
Laser Melting ◽  
Powder Bed ◽  
Part Quality ◽  
Melt Pool ◽  
Wide Range ◽  
Am Processes

Additive Manufacturing (AM) processes enable their deployment in broad applications from aerospace to art, design, and architecture. Part quality and performance are the main concerns during AM processes execution that the achievement of adequate characteristics can be guaranteed, considering a wide range of influencing factors, such as process parameters, material, environment, measurement, and operators training. Investigating the effects of not only the influential AM processes variables but also their interactions and coupled impacts are essential to process optimization which requires huge efforts to be made. Therefore, numerical simulation can be an effective tool that facilities the evaluation of the AM processes principles. Selective Laser Melting (SLM) is a widespread Powder Bed Fusion (PBF) AM process that due to its superior advantages, such as capability to print complex and highly customized components, which leads to an increasing attention paid by industries and academia. Temperature distribution and melt pool dynamics have paramount importance to be well simulated and correlated by part quality in terms of surface finish, induced residual stress and microstructure evolution during SLM. Summarizing numerical simulations of SLM in this survey is pointed out as one important research perspective as well as exploring the contribution of adopted approaches and practices. This review survey has been organized to give an overview of AM processes such as extrusion, photopolymerization, material jetting, laminated object manufacturing, and powder bed fusion. And in particular is targeted to discuss the conducted numerical simulation of SLM to illustrate a uniform picture of existing nonproprietary approaches to predict the heat transfer, melt pool behavior, microstructure and residual stresses analysis.

Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Paul Witherell ◽  
Shaw Feng ◽  
Timothy W. Simpson ◽  
David B. Saint John ◽  
Pan Michaleris ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Metal Powder ◽  
Manufacturing Process ◽  
Process Model ◽  
Model Development ◽  
Process Models ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Future Work ◽  
Am Processes

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.

New Hammerstein Modeling and Analysis for Controlling Melt Pool Width in Powder Bed Fusion Additive Manufacturing

2021 ◽  
pp. 1-7
Author(s):  
Dan Wang ◽  
Xinyu Zhao ◽  
Xu Chen
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Process Variations ◽  
Element Model ◽  
Hammerstein Model ◽  
Powder Bed Fusion ◽  
Modeling And Analysis ◽  
Powder Bed ◽  
Melt Pool ◽  
Linearized Model

Abstract Despite the advantages and emerging applications, broader adoption of powder bed fusion (PBF) additive manufacturing is challenged by insufficient reliability and in-process variations. Finite element modeling and control-oriented modeling have been identified fundamental for predicting and engineering part qualities in PBF. This paper first builds a finite element model (FEM) of the thermal fields to look into the convoluted thermal interactions during the PBF process. Using the FEM data, we identify a novel surrogate system model from the laser power to the melt pool width. Linking a linearized model with a memoryless nonlinear submodel, we develop a physics-based Hammerstein model that captures the complex spatiotemporal thermomechanical dynamics. We verify the accuracy of the Hammerstein model using the FEM and prove that the linearized model is only a representation of the Hammerstein model around the equilibrium point. Along the way, we conduct the stability and robustness analyses and formalize the Hammerstein model to facilitate the subsequent control designs.

Hybrid Modeling Approach for Melt Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Tesfaye Moges ◽  
Zhuo Yang ◽  
Kevontrez Jones ◽  
Shaw Feng ◽  
Paul Witherell ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Hybrid Model ◽  
Hybrid Models ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Powder Bed Fusion ◽  
Cfd Model ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

Abstract Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process-structure-property-performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser powder bed fusion (L-PBF) processes. Our unbiased, model integration method combines physics-based data and measurement data for approaching more accurate prediction of melt-pool width. Both a high-fidelity computational fluid dynamics (CFD) model and experiments utilizing optical images are used to generate a combined dataset of melt-pool widths. From this aggregated dataset, a hybrid model is developed using data-driven modeling techniques, including polynomial regression and Kriging methods. The performance of the hybrid model is evaluated by computing the average relative error and comparing it with the results of the simulations and surrogate models constructed from the original CFD model and experimental measurements. It is found that the proposed hybrid model performs better in terms of prediction accuracy and computational time. Future work includes a conceptual introduction on the use of an AM ontology to support improved model and data selection when constructing hybrid models. This study can be viewed as a significant step towards the use of hybrid models as predictive models with improved accuracy and without the sacrifice of speed.

Ultrasonics for monitoring melt pool dynamics and in situ sensing of microstructure during powder bed fusion additive manufacturing

2021 ◽  
Vol 150 (4) ◽  
pp. A307-A307
Author(s):  
Christopher M. Kube ◽  
Nathan Kizer ◽  
Abdalla Nassar ◽  
Edward Reutzel ◽  
Haifeng Zhang ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
In Situ Sensing

Fundamental Requirements for Data Representations in Laser-Based Powder Bed Fusion

2015 ◽  
Author(s):  
Shaw C. Feng ◽  
Paul W. Witherell ◽  
Gaurav Ameta ◽  
Duck Bong Kim
Keyword(s):  
Additive Manufacturing ◽  
Heterogeneous Systems ◽  
Powder Bed Fusion ◽  
Process Data ◽  
Data Interoperability ◽  
Powder Bed ◽  
Related Data ◽  
Data Representations ◽  
Am Processes

Additive Manufacturing (AM) processes intertwine aspects of many different engineering-related disciplines, such as material metrology, design, in-situ and off-line measurements, and controls. Due to the increasing complexity of AM systems and processes, data cannot be shared among heterogeneous systems because of a lack of a common vocabulary and data interoperability methods. This paper aims to address insufficiencies in laser-based Powder Bed Fusion (PBF), a specific AM process, data representations to improve data management and reuse in PBF. Our approach is to formally decompose the processes and align PBF process-specifics with information elements as fundamental requirements for representing process-related data. The paper defines the organization and flow of process information. After modeling selected PBF processes and sub-processes as activities, we discuss requirements for the development of more advanced process data models that provide common terminology and process knowledge for managing data from various stages in AM.

scholarly journals On the effect of shielding gas flow on porosity and melt pool geometry in laser powder bed fusion additive manufacturing

2020 ◽  
Vol 32 ◽  
pp. 101030 ◽  
Author(s):  
Joni Reijonen ◽  
Alejandro Revuelta ◽  
Tuomas Riipinen ◽  
Kimmo Ruusuvuori ◽  
Pasi Puukko
Keyword(s):  
Additive Manufacturing ◽  
Gas Flow ◽  
Powder Bed Fusion ◽  
Shielding Gas ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

Insights on Laser Additive Manufacturing of Invar 36

2020 ◽  
pp. 71-93
Author(s):  
Mostafa Yakout ◽  
M. A. Elbestawi
Keyword(s):  
Mechanical Properties ◽  
Thermal Expansion ◽  
Additive Manufacturing ◽  
Coefficient Of Thermal Expansion ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Laser Additive Manufacturing ◽  
Powder Bed ◽  
Iron Nickel ◽  
Am Processes

Recently, additive manufacturing (AM) became a promising technology to manufacture complex structures with acceptable mechanical properties. The laser powder-bed fusion (L-PBF) process is one of the most common AM processes that has been used for producing a wide variety of metals and composites. Invar 36 is an austenite iron-nickel alloy that has a very low coefficient of thermal expansion; therefore, it is a good candidate for the L-PBF process. This chapter covers the state-of-the-art for producing Invar 36 using the L-PBF process. The chapter aims at describing research insights of using metal AM techniques in producing Invar 36 components. Like most of nickel-based alloys, Invar 36 is weldable but hard-to-machine. However, there are some challenges while processing these alloys by laser. This chapter also covers the challenges of using the L-PBF process for producing nickel-based alloys. In addition, it reports the L-PBF conditions that could be used to produce fully dense Invar 36 components with mechanical properties comparable to the wrought Invar 36.

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