Simulation of temperature distribution in single metallic powder layer for laser micro-sintering

Yin, Junhui; Zhu, Haihong; Liu, Ke; Lei, Wenjuan; Cheng, Dan; Zuo, Duluo

doi:10.1016/j.commatsci.2011.09.012

Cited by 146 publications

(78 citation statements)

References 21 publications

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“…They introduced a unique simulation technique in an attempt to reproduce the complexity created with considering several powder layers. Yin et al [5] developed a simulation of the temperature distribution for laser micro-sintering (LMS). They analyzed the effect of laser power, beam size, scan speed on the temperature reached and the geometry of the melt pool.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In laser melting process, the powder material is completely melted and solidified, as opposed to laser sintering processes where metal powder material is sintered, or partially melted [5,8,11]. Both groups of processes utilize similar operational set-ups typically requiring a high power fiber laser source, a beam delivery lens system, a scanning mirror, a metal powder supply, a recoater roller or blade, and a build platform, (see Fig.…”

Section: Introductionmentioning

confidence: 99%

“…However, the build part quality and process performance, structural integrity, mechanical properties and related processing times are not at the desired industryready levels. Predictive process modeling and optimization for improved dimensional quality, product reliability, and overall productivity are of great interest to the current, ongoing research efforts [3][4][5][6][7]. This work gives a rapid calculation technique for predicting temperature profile and melt depth of metal powder material during laser melting.…”

Section: Introductionmentioning

confidence: 99%

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Temperature profile and melt depth in laser powder bed fusion of Ti-6Al-4V titanium alloy

Criales

Özel

2017

Prog Addit Manuf

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In this paper, the prediction of temperature profile and melt depth for laser powder bed fusion (L-PBF) of Ti-6Al-4V titanium powder material was performed by numerically solving the heat conduction-diffusion equation using a finite difference method. A review of the literature in numerical modeling for laser-based additive metal manufacturing is presented. Initially, the temperature profile along the depth direction into the powder material is calculated for a stationary single pulse laser heat source to understand the transient behavior of the temperature rise during L-PBF. The effect of varying laser pulse energy, average power, and the powder material's density is analyzed. A method to calculate and predict the maximum depth at which localized melting of the powder material occurs is provided.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Temperature profile and melt depth in laser powder bed fusion of Ti-6Al-4V titanium alloy

Criales

Özel

2017

Prog Addit Manuf

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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]. Although 1D and 2D FEA models have advantages in saving computation time and illustrating the key characteristics, a 3D model can better reflect the actual AM processes by considering the interaction among layers [54].…”

Section: Finite Element Analysismentioning

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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“…However, length (in the scan direction), depth, width, and area values are sometimes used to relate to process parameters. Often in AM modeling literature, a plot of the melt-pool temperature vs. some cross-section distance is given [119], [138]- [140]. Melt-pool size may be inferred and related to input parameters, though it is not often expressed as a single-value measurand (e.g., the melt-pool is x mm).…”

Section: Parameter-signature-quality Relationshipsmentioning

confidence: 99%

A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes

Mani

Lane

Donmez

et al. 2016

International Journal of Production Research

172

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Simulation of temperature distribution in single metallic powder layer for laser micro-sintering

Cited by 146 publications

References 21 publications

Temperature profile and melt depth in laser powder bed fusion of Ti-6Al-4V titanium alloy

Temperature profile and melt depth in laser powder bed fusion of Ti-6Al-4V titanium alloy

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

A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes

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