Thermal modeling of Inconel 718 processed with powder bed fusion and experimental validation using in situ measurements

Denlinger, Erik R.; Jagdale, Vijay; Srinivasan, G. V.; El-Wardany, Tahany; Michaleris, P.

doi:10.1016/j.addma.2016.03.003

Cited by 77 publications

(55 citation statements)

References 23 publications

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“…Heat exchange [7] Machine environnent temperature, Table 10 Mesh parameters for the application case.…”

Section: Properties Valuesmentioning

confidence: 99%

“…Besides, end-use mechanical properties are also a point of interest and determined by the final microstructure and the porosities of pieces [6,7]. Generally speaking, studies on numerical modelling work of SLM as reported in the literature are actually developed at various scales and usually considering several physical phenomena, leading to complex models.…”

Section: Introductionmentioning

confidence: 99%

“…For such macroscale modelling, regular or simple shape of the workpiece is usually considered when the powder bed is taken into account [7,15], while the powder bed is rarely considered when complex workpieces are addressed due to the difficulties to consider part/powder interfaces in such practical cases.…”

Section: Introductionmentioning

confidence: 99%

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Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process

Zhang

Guillemot

Bernacki

et al. 2018

Computer Methods in Applied Mechanics and Engineering

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A 3D finite element model is developed to study heat exchange during metal selective laser melting (SLM). The approach is conducted on the scale of the part to be formed, using a level set framework to track the interface between the constructed workpiece and non-melted powder, and interface between the gas domain and the successive powder bed layers. In order to keep sustainable the computational efficiency, the powder bed deposition and the energy input are simplified by the scale of an entire layer or fractions of each layer. Layer fractions are identified directly from a description of the global laser scan plan of the part to be built. Each fraction is heated during a time interval corresponding to the exposure time to the laser beam, and then cooled down during a time interval equal to the scan time for the considered layer fraction. The global heat transfer through the part under additive construction and through the powder material non-exposed to the laser beam is simulated. To reduce the computational cost, a refining and de-refining mesh adaptation is carried out with a conform mesh strategy. Mesh sensitivity tests and validation of energy conservation are discussed. The proposed model is able to predict the temperature distribution and evolution in the constructed workpiece and non-melted powder during the SLM process at the macroscale, for parts of complex geometry.Application is shown for a nickel based alloy (IN718), but the numerical model can be easily extended to other materials by using their data sets.

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“…Heat exchange [7] Machine environnent temperature, Table 10 Mesh parameters for the application case.…”

Section: Properties Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process

Zhang

Guillemot

Bernacki

et al. 2018

Computer Methods in Applied Mechanics and Engineering

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“…The governing equation to find the temperature distribution u in time is the balance of energy equation, expressed as

C false(u false) \partial_{t} u - bold-italic\nabla \cdot false(k false(u false) \nabla u false) = f, in normalΩ false(t false), t \in false[t_{normali}, t_{normalf} false],

where C ( u ) is the heat capacity coefficient, k ( u ) ≥ 0 is the thermal conductivity, and f is the rate of energy supplied to the system per unit volume by the moving laser. C ( u ) is given by the product of the density of the material ρ ( u ) and the specific heat c ( u ), but one may consider a modified heat capacity coefficient to also account for phase change effects or compute C ( u ) with the CALPHAD approach …”

Section: Application To Metal Ammentioning

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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“…For PBF processes, an additional conduction surface boundary condition consideration is for the un-melted powder bed particles surrounding the built structure. Some authors have neglected this condition to reduce computation time (by assuming an insulated outer surface), but Denlinger et al (2016) demonstrated that not including it can lead to 30% higher temperatures compared to experimental values, and even higher for larger components, suggesting that accommodation of this condition is critical for modeling additive processes for functional metal components [72]. Furthermore, because the radiation boundary condition is nonlinear in temperature, it is sometimes neglected in models to increase computational efficiency with the assumption that the dominant mode of heat loss is through conduction to the substrate [73,74].…”

Section: Modeling and Simulation Of Metal-based Additive Manufacturinmentioning

confidence: 99%

Invited review article: Metal-additive manufacturing—Modeling strategies for application-optimized designs

Bandyopadhyay

Traxel

2018

Additive Manufacturing

128

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Next generation, additively-manufactured metallic parts will be designed with application-optimized geometry, composition, and functionality. Manufacturers and researchers have investigated various techniques for increasing the reliability of the metal-AM process to create these components, however, understanding and manipulating the complex phenomena that occurs within the printed component during processing remains a formidable challenge—limiting the use of these unique design capabilities. Among various approaches, thermomechanical modeling has emerged as a technique for increasing the reliability of metal-AM processes, however, most literature is specialized and challenging to interpret for users unfamiliar with numerical modeling techniques. This review article highlights fundamental modeling strategies, considerations, and results, as well as validation techniques using experimental data. A discussion of emerging research areas where simulation will enhance the metal-AM optimization process is presented, as well as a potential modeling workflow for process optimization. This review is envisioned to provide an essential framework on modeling techniques to supplement the experimental optimization process.

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Thermal modeling of Inconel 718 processed with powder bed fusion and experimental validation using in situ measurements

Cited by 77 publications

References 23 publications

Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process

Macroscopic thermal finite element modeling of additive metal manufacturing by selective laser melting process

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

Invited review article: Metal-additive manufacturing—Modeling strategies for application-optimized designs

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