An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics

Thompson, Scott M.; Bian, Linkan; Shamsaei, Nima; Yadollahi, Aref

doi:10.1016/j.addma.2015.07.001

Cited by 660 publications

(464 citation statements)

References 134 publications

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“…Each value range is fixed starting from process parameters commonly adopted in DED [9]. The experimental results are investigated, taking into account a confidence interval of 95% and running three repetitions for each parameter setting.…”

Section: Methodsmentioning

confidence: 99%

“…Multiple nozzle deposition is suitable for parts with high geometrical complexity, even if it is more difficult to ensure flow uniformity and direct the powder to a specific region of interest [9]. In the case of both continuous coaxial configuration and discontinuous coaxial configuration, the spot of the powder flow in correspondence with the deposition plane usually ranges between 5 mm and 8 mm [6,[10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Approach to Evaluate the Performances of a New Deposition Nozzle Solution for DED Systems

et al. 2017

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Abstract:In order to improve the process efficiency of a direct energy deposition (DED) system, closed loop control systems can be considered for monitoring the deposition and melting processes and adjusting the process parameters in real-time. In this paper, the monitoring of a new deposition nozzle solution for DED systems is approached through a simulation-experimental comparison. The shape of the powder flow at the exit of the nozzle outlet and the spread of the powder particles on the deposition plane are analyzed through 2D images of the powder flow obtained by monitoring the powder depositions with a high-speed camera. These experimental results are then compared with data obtained through a Computational Fluid Dynamics model. Preliminary tests are carried out by varying powder, carrier, and shielding mass flow, demonstrating that the last parameter has a significant influence on the powder distribution and powder flow geometry.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Approach to Evaluate the Performances of a New Deposition Nozzle Solution for DED Systems

et al. 2017

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“…Figure 2. Finite element analysis steps in additive manufacturing processes [67][68][69][70][71][72][73][74][75][76].…”

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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“…The direct energy deposition (DED) technologies, based on powder projection or filler w ire m elting, a re m ore s uited f or the fabrication of bigger components due to its high deposition rate [5]. The heat source is often a laser beam [6], but can be also a plasma [7,8], or an electrical arc [9].…”

Section: Introductionmentioning

confidence: 99%

Effect of process parameters on the quality of aluminium alloy Al5Si deposits in wire and arc additive manufacturing using a cold metal transfer process

Gómez-Ortega

Galvan

Deschaux-Beaume

et al. 2018

Science and Technology of Welding and Joining

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Rouquette. Effect of process parameters on the quality of aluminium alloy Al5Si deposits in wire and arc additive manufacturing using a cold metal transfer process. Science and Technology of Welding and Joining, Maney Publishing, 2017, 23 (4), pp.316 -332. <10.1080/13621718.2017 Effect of process parameters on the quality of aluminium alloy Al5Si deposits in wire and arc additive manufacturing using a cold metal transfer process A. Gomez Ortega, L. Corona Galvan, F. Deschaux-Beaume, B. Mezrag and S. Rouquette LMGC, Univ. Montpellier, CNRS, Montpellier, France ABSTRACT A 3D print device using a cold metal transfer arc welding station to melt a metallic filler wire is developed to build aluminium part by optimising the process parameters. First tests achieved using standard pre-recorded process parameters allow to study the effect of the travel speed and the average welding power on the geometrical characteristics of mono-layer deposits and on walls built by layers superposition. Finally, a parametric study of the effect of each process parameter controlling the shape of the arc current or voltage and the filler wire feeding is carried out in order to try to improve the geometrical regularity of the deposits, and to better understand the effect of each parameter on the melting of the filler wire, its transfer on the support plate, and the geometry of the formed bead.

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An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics

Cited by 660 publications

References 134 publications

Monitoring Approach to Evaluate the Performances of a New Deposition Nozzle Solution for DED Systems

Monitoring Approach to Evaluate the Performances of a New Deposition Nozzle Solution for DED Systems

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

Effect of process parameters on the quality of aluminium alloy Al5Si deposits in wire and arc additive manufacturing using a cold metal transfer process

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