Numerical prediction of temperature and density distributions in selective laser sintering processes

Cervera, Miguel; Lombera, Guillermo

doi:10.1108/13552549910251846

Cited by 142 publications

(22 citation statements)

References 12 publications

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“…Through ray-tracing algorithms, the trajectories of the light rays, or photons, emitted by the laser source can be simulated probabilistically when travelling into the considered medium, until they hit a pre-defined area. According to the model proposed by Xin et al [93], the initial position of a photon in the laser beam section is defined in spherical coordinates by the radius and the azimuthal angle u, where the angle is distributed uniformly in the interval 0; 2p ½ , while the radius [93,94] Particle-based Semi-crystalline (PA12) Thermal [87] Analytical Semi-crystalline (PVA) Thermal [88,96] Finite elements Semi-crystalline (PA6) Thermal [89,90] Finite elements Semi-crystalline (PA12) Thermal, sintering [80,82,83,86] Finite elements Amorphous (PC) Thermal, sintering [79,81] Finite differences Amorphous (PC) Thermal, sintering [84] Finite volumes Amorphous (PC) Thermal, sintering [85,90] Finite elements Semi-crystalline (PA12) Optical, thermal, sintering [93] Particle-based Semi-crystalline (PA12) Thermal, mechanical [91,128] Finite elements Semi-crystalline (PA12) Thermal, mechanical [92] Finite elements Semi-crystalline (PP)…”

Section: Models Of the Thermal Processmentioning

confidence: 99%

Laser-based additively manufactured polymers: a review on processes and mechanical models

et al. 2020

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Additive manufacturing (AM) is a broad definition of various techniques to produce layer-by-layer objects made of different materials. In this paper, a comprehensive review of laser-based technologies for polymers, including powder bed fusion processes [e.g. selective laser sintering (SLS)] and vat photopolymerisation [e.g. stereolithography (SLA)], is presented, where both the techniques employ a laser source to either melt or cure a raw polymeric material. The aim of the review is twofold: (1) to present the principal theoretical models adopted in the literature to simulate the complex physical phenomena involved in the transformation of the raw material into AM objects and (2) to discuss the influence of process parameters on the physical final properties of the printed objects and in turn on their mechanical performance. The models being presented simulate: the thermal problem along with the thermally activated bonding through sintering of the polymeric powder in SLS; the binding induced by the curing mechanisms of light-induced polymerisation of the liquid material in SLA. Key physical variables in AM objects, such as porosity and degree of cure in SLS and SLA respectively, are discussed in relation to the manufacturing process parameters, as well as to the mechanical resistance and deformability of the objects themselves. Graphic abstract

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Section: Models Of the Thermal Processmentioning

confidence: 99%

Laser-based additively manufactured polymers: a review on processes and mechanical models

et al. 2020

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“…Most research has focused on thermal engineering and thermoforming. Bugeda et al [6] developed a finite element model for the 3D simulation of the sintering of a single track during a Selective Laser Sintering (SLS) process which takes into account both the thermal and the sintering phenomena involved in the process. Based on a series of single tracks, Song et al [7] firstly proposed the processing windows corresponding to different melting mechanisms in terms of microstructure, roughness, densification and microhardness.…”

Section: Introductionmentioning

confidence: 99%

Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

Wang

Zhang

et al. 2020

Applied Sciences

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This paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various infill patterns are generated for making comparisons. Inspired by monocular vision and even binocular vision, the mathematical model of thermal defect prediction via infrared thermogram is built via virtual printing of Digital Twins to preset the initial parameters of Artificial Neural Network (ANN). Particularly, the depth convolution is used to extract multi-scale features of layered image. By using transfer learning techniques to identify small sample data, the CGAN is employed to build the nonlinear implicit relations between thermal deformation and multi-scale features. The binocular stereo vision laser scanner is used to determine the actual thermal deformation of the target printed objects. The shape deformation dissimilarity can be succinctly calculated by evaluating the surface profile error via mesh registration between the original source and target mesh model. The proposed method is verified by physical experiments. The experiment proved that the proposed method can deal with the thermal deformation with more optimal parameters, which contributes to performance forward design of irregular complex parts regarding diversified customized requirements.

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“…Experience acquired in modelling traditional processes, such as casting or welding, has been the cornerstone of the first models for metal AM processes . At the part scale, finite element (FE) modelling has proved to be useful to assess the influence of process parameters, compute temperature distributions, or evaluate distortions and residual stresses .…”

Section: Introductionmentioning

confidence: 99%

“…Experience acquired in modelling traditional processes, such as casting or welding, [4][5][6] has been the cornerstone of the first models for metal AM processes. [7][8][9][10][11] At the part scale, finite element (FE) modelling has proved to be useful to assess the influence of process parameters, 12 compute temperature distributions, 13,14 or evaluate distortions and residual stresses. [15][16][17] Recent contributions have introduced microstructure simulations of grain growth 18,19 and crystal plasticity, 20 melt-pool-scale models 3,21 and even multiscale and multiphysics solvers.…”

Section: Introductionmentioning

confidence: 99%

A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

Neiva

Badia

Martı́n

et al. 2019

Numerical Meth Engineering

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Summary This work introduces an innovative parallel fully‐distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well known that virtual part design and qualification in additive manufacturing requires highly accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time‐to‐market. However, efficiency, without loss of accuracy, has rarely held the centre stage in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high‐end distributed‐memory machines. It is grounded on three building blocks: (1) hierarchical adaptive mesh refinement with octree‐based meshes; (2) a parallel strategy to model the growth of the geometry; and (3) state‐of‐the‐art parallel iterative linear solvers. Computational experiments consider the heat transfer analysis at the part scale of the printing process by powder‐bed technologies. After verification against a three‐dimensional (3D) benchmark, a strong‐scaling analysis assesses performance and identifies major sources of parallel overhead. A third numerical example examines the efficiency and robustness of (2) in a curved 3D shape. Unprecedented parallelism and scalability were achieved in this work. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part‐scale simulations of metal or polymer additive manufacturing but also in welding, sedimentation, atherosclerosis, or any other physical problem where the physical domain of interest grows in time.

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Numerical prediction of temperature and density distributions in selective laser sintering processes

Cited by 142 publications

References 12 publications

Laser-based additively manufactured polymers: a review on processes and mechanical models

Laser-based additively manufactured polymers: a review on processes and mechanical models

Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

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