A review of multi-scale and multi-physics simulations of metal additive manufacturing processes with focus on modeling strategies

Bayat, Mohamad; Dong, Wenxia; Thorborg, Jesper; To, Albert C.; Hattel, Jesper Henri

doi:10.1016/j.addma.2021.102278

Cited by 76 publications

(46 citation statements)

References 145 publications

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“…Then, the temperature data are used in residual stress analyses until all steps are finished. The mesoscopic models used for highly varied and anisotropic characteristics are generally below the millimeter level [ 23 , 24 ]. The macro-model is still the major approach for part-level simulation.…”

Section: Finite Element Model Methodologymentioning

confidence: 99%

Thermal and Mechanical Variation Analysis on Multi-Layer Thin Wall during Continuous Laser Deposition, Continuous Pulsed Laser Deposition, and Interval Pulsed Laser Deposition

Kong

Liang

et al. 2022

Materials

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Direct laser deposition (DLD) is widely used in precision manufacturing, but the process parameters (e.g., laser power, scanning patterns) easily lead to changes in dimensional accuracy and structural properties. Many methods have been proposed to analyze the principle of distortion and residual stress generation, but it is difficult to evaluate the involvement of temperature and stress in the process of rapid melting and solidification. In this paper, a three-dimensional finite element model is established based on thermal–mechanical relationships in multilayer DLD. Differences in temperature and residual stress between continuous laser deposition (CLD) and pulsed laser deposition (PLD) are compared with the numerical model. To validate the relationship, the temperature and residual stress values obtained by numerical simulation are compared with the values obtained by the HIOKI-LR8431 temperature logger and the Pulstec μ-X360s X-ray diffraction (XRD) instrument. The results indicate that the temperature and residual stress of the deposition part can be evaluated by the proposed simulation model. The proposed PLD process, which includes continuous pulsed laser deposition (CPLD) and interval pulsed laser deposition (IPLD), were found more effective to improve the homogeneity of temperature and residual stress than the CLD process. This study is expected to be useful in the distortion control and microstructure consistency of multilayer deposited parts.

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Section: Finite Element Model Methodologymentioning

confidence: 99%

Thermal and Mechanical Variation Analysis on Multi-Layer Thin Wall during Continuous Laser Deposition, Continuous Pulsed Laser Deposition, and Interval Pulsed Laser Deposition

Kong

Liang

et al. 2022

Materials

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“…In this regard, simulations of AM processes have been recently pursued to optimize process parameters and to predict the formation of defects occurring in and close to the melt pool and to find out a way to avoid their formation [6,7]. Thus, process optimization could be achieved without in-line production trials, leading to considerable economic advantages and timesaving.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Melt Pool and Porosity During Laser Powder Bed Fusion of Ti6Al4V Alloy: Numerical Modelling and Experimental Validation

Ransenigo

Tocci

Palo³

et al. 2022

Lasers Manuf. Mater. Process.

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Laser powder bed fusion (L-PBF) is one of the most promising additive manufacturing technologies for metals. In this work, the discrete element method (DEM) was used to reproduce a powder bed of particles distributed in a random way to be as close as possible to reality. Single and multiple scan tracks simulations were performed for Ti6Al4V alloy using a commercial CFD software, FLOW-3D AM®. The output from the numerical simulations was elaborated to obtain shape and size of melt pools, morphology of scan track surfaces and porosity content. In particular, a specific model was used in order to predict air entrainment in the melt pool and, therefore, to estimate gas porosity content, as an innovative approach to predict such defects. Results from simulations were compared with experimental data from Ti6Al4V samples produced by L-PBF in terms of melt pools size and morphology, as well as density. The good agreement between calculated and experimental results indicates that simulation of L-PBF can represent a powerful tool not only for the optimization of process parameters, but also for the prediction of porosity level.

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“…To determine the optimal processing window for part production with minimal defects, various experimental monitoring techniques are often implemented. These methods, are divided into ex-situ and in-situ monitoring techniques, based on whether it is conducted after the part has been produced or while the component is being manufactured, respectively [11]. These monitoring techniques can be employed to gain further insight into the generation mechanism of these defects.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, utilizing ML methods in additive manufacturing improves product quality, optimizes manufacturing processing parameters, and reduces cost [19]. Furthermore, in comparison with the experimental techniques that are solely aimed and designed for only a specific subject, ML models are much more flexible and can be easily adjusted upon the application [11].…”

Section: Introductionmentioning

confidence: 99%

MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning

Parand¹,

Ogoke²,

Kao³

et al. 2022

Preprint

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Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on process parameters and powder material is difficult due to the complex nature of MAM process. Machine learning (ML) techniques can be useful in connecting process parameters to the type of flaws in the meltpool. In this work, we introduced a comprehensive framework for benchmarking ML for melt pool characterization. An extensive experimental dataset has been collected from more than 80 MAM articles containing MAM processing conditions, materials, meltpool dimensions, meltpool modes and flaw types. We introduced physics-aware MAM featurization, versatile ML models, and evaluation metrics to create a comprehensive learning framework for meltpool defect and geometry prediction. This benchmark can serve as a basis for melt pool control and process optimization. In addition, data-driven explicit models have been identified to estimate meltpool geometry from process parameters and material properties which outperform Rosenthal estimation for meltpool geometry while maintaining interpretability.

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A review of multi-scale and multi-physics simulations of metal additive manufacturing processes with focus on modeling strategies

Cited by 76 publications

References 145 publications

Thermal and Mechanical Variation Analysis on Multi-Layer Thin Wall during Continuous Laser Deposition, Continuous Pulsed Laser Deposition, and Interval Pulsed Laser Deposition

Thermal and Mechanical Variation Analysis on Multi-Layer Thin Wall during Continuous Laser Deposition, Continuous Pulsed Laser Deposition, and Interval Pulsed Laser Deposition

Evolution of Melt Pool and Porosity During Laser Powder Bed Fusion of Ti6Al4V Alloy: Numerical Modelling and Experimental Validation

MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning

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