A three-dimensional finite element analysis of the temperature field during laser melting of metal powders in additive layer manufacturing

Roberts, Ibiye A.; Wang, Changjiang; Esterlein, R.; Stanford, Mark; Mynors, Diane J.

doi:10.1016/j.ijmachtools.2009.07.004

Cited by 579 publications

(278 citation statements)

References 20 publications

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“…To compare with the previous work in literature, a selective laser sintering (SLS) process is investigated in reference [15] based on continuous media theory, where the maximum temperature value is compared between simulations and experiments with a relative prediction error percentage more than 5.00%. In reference [141], the maximum temperature during laser melting of metal powder is evaluated and the relative error of the FEA results from the experimental results [142] is 2.8%. It should be noted that the work in these references does not enable component-scale prediction, let alone the higher prediction error and higher computational cost as compared to the proposed framework.…”

Section: Prediction For Different Component Geometries Using Trainingmentioning

confidence: 99%

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Jin

2018

Materials & Design

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A quantitative understanding of thermal field evolution is vital for quality control in additive manufacturing (AM). Because of the unknown material parameters, high computational costs, and imperfect understanding of the underlying science, physically-based approaches alone are insufficient for component-scale thermal field prediction. Here, I present a new framework that integrates physically-based and data-driven approaches with quasi in situ thermal imaging to address this problem. The framework consists of (i) thermal modeling using 3D finite element analysis (FEA), (ii) surrogate modeling using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the computational costs from the high-fidelity, physically-based model. I finally employ a Bayesian calibration method, which incorporates the surrogate model and thermal measurements, to enable layer-to-layer thermal field prediction across the whole component. A case study on fused deposition modeling is conducted for components with 7 to 16 layers. The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs. Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive ManufacturingJingran Li GENERAL AUDIENCE ABSTRACT This paper aims to achieve the layer to layer temperature monitoring and consequently predict the temperature distribution for any new freeform geometry. An engineering statistical synergistic model is proposed to integrate the pure statistical methods and finite element modeling (FEM), which is physically meaningful as well as accurate for temperature prediction. Besides, this proposed synergistic model contains geometry information, which can be applied to any freeform geometry. This paper serves to enable a holistic cyber physical systems-based approach for the additive manufacturing (AM) not only restricted in fused deposition modeling (FDM) process but also can be extended to powder-based process like laser engineered net shaping (LENS) and selective laser sintering (SLS). This paper as well as the scheduled future works will make it affordable for customized AM including customized geometries and materials, which will greatly accelerate the transition from rapid prototyping to rapid manufacturing. This article demonstrates a first evaluation of engineering statistical synergistic model in AM technology, which gives a perspective on future researches about online quality monitoring and control of AM based data fusion principles. iv ACKNOWLEDGEMENTS

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Section: Prediction For Different Component Geometries Using Trainingmentioning

confidence: 99%

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Jin

2018

Materials & Design

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“…Much of our detailed understanding of these fields come from applications of the finite element method (FEM), which has been used extensively to model them. [8][9][10][11][12][13] For solidification in the LENS™ process in particular, Hofmeister [8] used a combination of thermal imaging and finite element modeling to estimate cooling rates and thermal gradients in the molten pool for various sets of process parameters. Similarly, Wang and Felicelli [10] used a 2D finite element model to calculate temperature as a function of time in different regions of the molten pool for the deposition of a thin plate structure with a moving beam, a work later expanded to 3D by the same authors.…”

Section: The Laser Engineered Net Shaping (Lens™)mentioning

confidence: 99%

“…Specifically, the effects of epitaxial growth [26] as well as the movement of the laser beam [9,13,72] can have a significant effect on the dendrite orientations. Epitaxial growth leads to preferred initial growth in prescribed directions, which may not align with the large thermal gradients.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Ti-W Solidification Microstructures Under Additive Manufacturing Conditions

Rolchigo

Mendoza

Samimi

et al. 2017

Metall Mater Trans A

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Additive manufacturing (AM) processes have many benefits for the fabrication of alloy parts, including the potential for greater microstructural control and targeted properties than traditional metallurgy processes. To accelerate utilization of this process to produce such parts, an effective computational modeling approach to identify the relationships between material and process parameters, microstructure, and part properties is essential. Development of such a model requires accounting for the many factors in play during this process, including laser absorption, material addition and melting, fluid flow, various modes of heat transport, and solidification. In this paper, we start with a more modest goal, to create a multiscale model for a specific AM process, Laser Engineered Net Shaping (LENS™), which couples a continuumlevel description of a simplified beam melting problem (coupling heat absorption, heat transport, and fluid flow) with a Lattice Boltzmann-cellular automata (LB-CA) microscale model of combined fluid flow, solute transport, and solidification. We apply this model to a binary Ti-5.5 wt pct W alloy and compare calculated quantities, such as dendrite arm spacing, with experimental results reported in a companion paper.

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“…Several published studies explicitly described the specific mechanics of the stress formulation as described above, most notably in the works of Mercelis and Kruth [9] and Knowles et al [8]. Other studies which discussed this issue in depth were those by Roberts et al [16][17], Matsumoto et al [18], Gu et al [19], Guo and Leu [4], and Van Belle et al [20].…”

Section: Figure 1 Slm/dmls Process Mechanicsmentioning

confidence: 99%

Overhanging Features and the SLM/DMLS Residual Stresses Problem: Review and Future Research Need

Patterson¹,

Messimer²,

Farrington³

2017

Preprint

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A useful and increasingly common additive manufacturing (AM) process is the selective laser melting (SLM) or direct metal laser sintering (DMLS) process. SLM/DMLS can produce fulldensity metal parts from difficult materials, but it tends to suffer from severe residual stresses introduced during processing. This limits the usefulness and applicability of the process, particularly in the fabrication of parts with delicate overhanging and protruding features. The purpose of this study was to examine the current insight and progress made toward understanding and eliminating the problem in overhanging and protruding structures. To accomplish this, a survey of literature was undertaken, focusing on process modeling (general, heat transfer, stress and distortion, and material models), direct process control (input and environmental control, hardware-in-the-loop monitoring, parameter optimization, and post-processing), experiment development (methods for evaluation, optical and mechanical process monitoring, imaging, and design-of-experiments), support structure optimization, and overhang feature design; approximately 140 published works were examined. The major findings of this study were that a small minority of the literature on SLM/DMLS deals explicitly with the overhanging stress problem, but some fundamental work has been done on the problem. Implications, needs, and potential future research directions are discussed in-depth in light of the present review.

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A three-dimensional finite element analysis of the temperature field during laser melting of metal powders in additive layer manufacturing

Cited by 579 publications

References 20 publications

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing

Modeling of Ti-W Solidification Microstructures Under Additive Manufacturing Conditions

Overhanging Features and the SLM/DMLS Residual Stresses Problem: Review and Future Research Need

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