Modeling and experimental validation of an immersed thermo-mechanical part-scale analysis for laser powder bed fusion processes

Carraturo, Massimo; Jomo, John N.; Kollmannsberger, Stefan; Reali, Alessandro; Auricchio, Ferdinando; Rank, Ernst

doi:10.1016/j.addma.2020.101498

Cited by 28 publications

(24 citation statements)

References 31 publications

(25 reference statements)

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“…The other process parameters related to the machine and the material are described in the following subsections. Further details can be found in [47,48].…”

Section: Application To An Additive Manufacturing Benchmark Testmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

Chaudhry

Soulaïmani

2022

Applied Sciences

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Selective laser melting (SLM) is a metal-based additive manufacturing (AM) technique. Many factors contribute to the output quality of SLM, particularly the machine and material parameters. Analysis of the parameters’ effects is critical, but using traditional experimental and numerical simulation can be expensive and time-consuming. This paper provides a framework to analyze the sensitivity and uncertainty in SLM input and output parameters, which can then be used to find the optimum parameters. The proposed data-driven approach combines machine learning algorithms with high-fidelity numerical simulations to study the SLM process more efficiently. We have considered laser speed, hatch spacing, layer thickness, Young modulus, and Poisson ratio as input variables, while the output variables are numerical predicted normal strains in the building part. A surrogate model was constructed with a deep neural network (DNN) or polynomial chaos expansion (PCE) to generate a response surface between the SLM output and the input variables. The surrogate model and the sensitivity analysis found that all five parameters were important in the process. The surrogate model was combined with non-intrusive optimization algorithms such as genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) to perform an inverse analysis and find the optimal parameters for the SLM process. Of the three algorithms, the PSO performed well, and the DNN model was found to be the most efficient surrogate model compared to the PCE.

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“…The other process parameters related to the machine and the material are described in the following subsections. Further details can be found in [47,48].…”

Section: Application To An Additive Manufacturing Benchmark Testmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

Chaudhry

Soulaïmani

2022

Applied Sciences

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“…A third technique is called the finite cell method and relaxes the constraint that each newly added layer must conform to the finite element discretization. Instead, layers may be initialized within elements [3,13]. Section 3.1 presents a corresponding formulation.…”

Section: The Thermal Problem An Its Discretizationmentioning

confidence: 99%

On accurate time integration for temperature evolutions in additive manufacturing

Kollmannsberger

Kopp

2021

GAMM-Mitteilungen

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We investigate two numerical challenges in thermal finite element simulations of laser powder bed fusion (LPBF) processes. First, we compare the behavior of first‐ and second‐order implicit time‐stepping schemes on a fixed domain. While both methods yield comparable accuracies in the pre‐asymptotic regime, the second‐order method eventually outperforms the first‐order method. However, the oscillations present in the pre‐asymptotic range of the second‐order method can render it less suitable for simulating LPBF processes. Then, we consider sudden domain extensions resulting from subsequently adding new layers of material with ambient temperature. We model this extension on the continuous level in an energy conservative manner. The discontinuities introduced here reduce the convergence order for both time‐stepping schemes to 0.75. First and second order accuracy could only be achieved by strongly grading the time‐steps towards the domain expansion.

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“…The multilayer and multitrack model developed by Ren et al [40] is used to not only explain the experimental observation of residual stress but also to study the reduction of distortion in additive manufacturing, where the simulation of the thermal stress uses a simplified homogeneous model. To predict the deflection and residual stress of components manufactured with PBF at large‐scale, part‐scale multilayer models were developed [4,6,11]. The simulation of the part‐scale model uses layers around 1 mm [11].…”

Section: Introductionmentioning

confidence: 99%

“…Since PBF‐manufactured components with complex geometry are considered in these models, generation of the analysis‐suitable mesh can also be problematic. Finite cell method instead of the finite element method was adopted to simulate the PBF process at part‐scale by means of a layer‐by‐layer activation process [4]. Few works directly considered mechanical analysis of the multilayer porous microstructure on the mesoscale, such as porous 316L stainless steel fabricated by SS [52].…”

Section: Introductionmentioning

confidence: 99%

3D‐multilayer simulation of microstructure and mechanical properties of porous materials by selective sintering

et al. 2021

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This work presents multilayer phase‐field simulation of selective sintering process and the calculation of effective mechanical properties and residual stress of the microstructure using the finite element method. The dependence of the effective properties and residual stress on the process parameters, such as beam power and scan speed, are analyzed. Significant partial melting of powders is observed for large beam power and low scan speed, which results in low porosity of the microstructure. Nonlinear relationship between the effective mechanical properties and process parameters is observed. The increasing rate of effective mechanical properties decreases with increasing beam power, while increases with decreasing scan speed. The dependence of effective Young's modulus and Poisson's ratio on porosity are well established using power law models. Stress concentrations are found at the necking region of powders and the intensity increases with the level of partial melting, which results in increasing residual stress in the microstructure. The numerical results reveal quantitatively the process‐microstructure‐property relation, which implies the feasibility of the subsequent data‐driven approach.

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Modeling and experimental validation of an immersed thermo-mechanical part-scale analysis for laser powder bed fusion processes

Cited by 28 publications

References 31 publications

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

On accurate time integration for temperature evolutions in additive manufacturing

3D‐multilayer simulation of microstructure and mechanical properties of porous materials by selective sintering

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