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

2021 ◽  
Author(s):  
Tesfaye Moges ◽  
Zhuo Yang ◽  
Kevontrez Jones ◽  
Shaw Feng ◽  
Paul Witherell ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Hybrid Modeling ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Modeling Approach ◽  
Melt Pool

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.

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

2021 ◽  
pp. 1-46
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.

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

Identifying Uncertainty in Laser Powder Bed Fusion Additive Manufacturing Models

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Felipe Lopez ◽  
Paul Witherell ◽  
Brandon Lane
Keyword(s):  
Additive Manufacturing ◽  
Model Uncertainty ◽  
Calibration Data ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Estimation Algorithms ◽  
Propagation Of Uncertainty ◽  
Melt Pool ◽  
Online Measurements

As additive manufacturing (AM) matures, models are beginning to take a more prominent stage in design and process planning. A limitation frequently encountered in AM models is a lack of indication about their precision and accuracy. Often overlooked, model uncertainty is required for validation of AM models, qualification of AM-produced parts, and uncertainty management. This paper presents a discussion on the origin and propagation of uncertainty in laser powder bed fusion (L-PBF) models. Four sources of uncertainty are identified: modeling assumptions, unknown simulation parameters, numerical approximations, and measurement error in calibration data. Techniques to quantify uncertainty in each source are presented briefly, along with estimation algorithms to diminish prediction uncertainty with the incorporation of online measurements. The methods are illustrated with a case study based on a thermal model designed for melt pool width predictions. Model uncertainty is quantified for single track experiments, and the effect of online estimation in overhanging structures is studied via simulation.

scholarly journals Investigation of laser-powder interaction in laser powder bed fusion process in additive manufacturing

2021 ◽  
Vol 249 ◽  
pp. 12002
Author(s):  
Erlei Li ◽  
Lin Wang ◽  
Ruiping Zou ◽  
Aibing Yu ◽  
Zongyan Zhou
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Computational Fluid Dynamics Cfd ◽  
Laser Rays ◽  
Physical Phenomena ◽  
Melting And Solidification ◽  
Packed Powder

Laser powder bed fusion (LPBF) is one of the most promising additive manufacturing (AM) technologies to fabricate metal components using laser beams. To understand the underlying thermal and physical phenomena in LPBF process, discrete element method (DEM) is applied to generate the randomly packed powder, then computational fluid dynamics (CFD) coupled with volume of fluid (VOF) is adopted to simulate the laser-powder interaction. The penetration and multiple reflection of laser rays is traced. The physics of melting and solidification is captured. The temperature profile indicates the laser travel path and the adsorption and transmission of laser rays with the powder. The wetting behaviour of the melt pool driven by the capillary forces leads to the formation of pores at the connection zone. It has been demonstrated that the developed model can capture the laser-powder interaction for further understanding of LPBF process.

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