Uncertainty Quantification of Mesoscale Melt-Pool Model for Powder Bed Fusion Additive Manufacturing of Metals

A simplified analytical model of the laser powder bed fusion (LPBF) process was used to develop a novel density prediction approach that can be adapted for any given powder feedstock and LPBF system. First, calibration coupons were built using IN625, Ti64 and Fe powders and a specific LPBF system. These coupons were manufactured using the predetermined ranges of laser power, scanning speed, hatching space, and layer thickness, and their densities were measured using conventional material characterization techniques. Next, a simplified melt pool model was used to calculate the melt pool dimensions for the selected sets of printing parameters. Both sets of data were then combined to predict the density of printed parts. This approach was additionally validated using the literature data on AlSi10Mg and 316L alloys, thus demonstrating that it can reliably be used to optimize the laser powder bed metal fusion process.

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A printability assessment framework for fabricating low variability nickel-niobium parts using laser powder bed fusion additive manufacturing

Rapid Prototyping Journal ◽

10.1108/rpj-01-2021-0024 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Bing Zhang ◽

Raiyan Seede ◽

Austin Whitt ◽

David Shoukr ◽

Xueqin Huang ◽

...

Keyword(s):

Mechanical Properties ◽

Additive Manufacturing ◽

Uncertainty Quantification ◽

Process Optimization ◽

Powder Bed Fusion ◽

Assessment Framework ◽

Laser Powder Bed Fusion ◽

Content Type ◽

Powder Bed ◽

Analytical Thermal Model

Purpose There is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM. Design/methodology/approach The printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework. Findings Near fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature. Originality/value Although many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.

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

ASME Letters in Dynamic Systems and Control ◽

10.1115/1.4050079 ◽

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.

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

Materials ◽

10.3390/ma13173895 ◽

2020 ◽

Vol 13 (17) ◽

pp. 3895 ◽

Cited By ~ 2

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.

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

Volume 9: 40th Computers and Information in Engineering Conference (CIE) ◽

10.1115/detc2020-22615 ◽

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.

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

Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing ◽

10.1115/msec2019-2991 ◽

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.

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