Modelling, simulation and experimental validation of heat transfer in selective laser melting of the polymeric material PA12

Riedlbauer, Daniel; Drexler, Maximilian; Drummer, Dietmar; Steinmann, Paul; Mergheim, Julia

doi:10.1016/j.commatsci.2014.06.046

Cited by 73 publications

(53 citation statements)

References 12 publications

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“…with P 0 being the beam power, x 0 the center of the beam and the value of the beam diameter being 4σ = 400 µm, which is a common diameter in SLS [7,13,14]. Each ray is initialized at the top of the simulation domain in the middle of a cell at the position x B and y B .…”

Section: Plain Surfacementioning

confidence: 99%

“…The resulting material and mechanical properties are dependent on the laser energy input which is dominated by the interaction of the beam with the powder bed [4,5]. While thermal modeling of the process is conducted with simplified absorption models [6,7], a mesoscopic model at the powder scale is still missing. Therefore, in this work, a ray tracing absorption model for thermoplastic powders is developed and validated with theoretical and experimental data.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Laser Beam Absorption in a Polymer Powder Bed

et al. 2018

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Abstract:In order to understand the absorption characteristic, a ray trace model is developed by taking into account the reflection, absorption and refraction. The ray paths are resolved on a sub-powder grid. For validation, the simulation results are compared to analytic solutions of the irradiation of the laser beam onto a plain surface. In addition, the absorptance, reflectance and transmittance of PA12 powder layers measured by an integration sphere setup are compared with the numerical results of our model. It is shown that the effective penetration depth can be lower than the penetration depth in bulk material for polymer powders and, therefore, can increase the energy density at the powder bed surface. The implications for modeling of the selective laser sintering (SLS) process and the processability of fine powder distributions and high powder bed densities are discussed.

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Section: Plain Surfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of Laser Beam Absorption in a Polymer Powder Bed

et al. 2018

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“…The physically-based modeling, like finite element analysis (FEA) simulation, has been applied to simulate thermal field evolution as well as calculating the local cooling rates [7]. The previous thermal field simulation researches mainly focused on the 1D model like a single track [49,50] or the 2D model like a single layer [51][52][53].…”

Section: Finite Element Analysismentioning

confidence: 99%

“…The physically-based modeling, like finite element analysis (FEA) simulation, has been applied to simulate thermal field evolution as well as calculating the local cooling rates [7] in AM. The physically-based modeling can help understand the manufacturing system through engineering knowledge, but always time consuming.…”

Section: Chapter 1: Introductionmentioning

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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“…The temperature distribution was also simulated in [5] and [6], where the influence of the laser power and velocity on the temperature distribution was studied. In [7], not only the influence of the laser parameters on the temperature was investigated, but also the resulting melting pool dimensions for PA12 powder. A nonlinear heat transfer model was used in [8] to compute the temperature distribution to analyze the heat and cooling rates of PA12 for various laser parameters.…”

Section: Introductionmentioning

confidence: 99%