Numerical methods for microstructural evolutions in laser additive manufacturing

Zhang, Zhao; Tan, Zhengquan; Yao, X.X.; Hu, Chao; Ge, P.; Wan, Zhongping; Li, J. Y.; Wu, Qi

doi:10.1016/j.camwa.2018.07.011

Cited by 53 publications

(24 citation statements)

References 47 publications

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“…For AM, microstructures and mechanical properties are key factors studied to monitor and enhance the consistency of the final product. [98][99][100][101][102] Rare data about the influence of laser beam scanning paths on the material's microstructures and mechanical properties exists in literature until now. Many scholars have studied the impact of scanning patterns on microstructure and mechanical properties in LBAM; their findings clearly state that the scanning pattern has a significant effect on microstructure and mechanical properties of LBAM fabricated components.…”

Section: Microstructure and Mechanical Propertiesmentioning

confidence: 99%

Review on scanning pattern evaluation in laser-based additive manufacturing

et al. 2021

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Laser-based additive manufacturing (LBAM) is a group of advanced manufacturing processes used to produce metal components and functionally graded products. Production in LBAM is either limited to the formation of thin or thick coatings on a substrate by laser metal deposition or the production of a fully functional metallic product by selective laser melting. In every case, LBAM fabricated components require optimization for the process parameters to avoid defects, such as porosity, crack holes, thermal deformation, and mechanical strength. As a key link in the laser additive manufacturing (LAM) process, laser scanning path planning is an effective strategy for balancing the temperature field of the formed part, avoiding stress concentration, and preventing deformation and cracking. Efficient, accurate, and reasonable planning of the laser scanning path is of great significance for improving the processing efficiency of the process data, prolonging the life of the laser scanning system, and improving the forming quality of the specimen. Through many studies, it was found that the scanning pattern of the lasers has a significant impact on the mechanical properties and deformations caused by a thermal mismatch during the process. Therefore, it is essential to have indepth knowledge about path planning in LBAM. Our review mainly focuses on the influence of scanning patterns on deformation, temperature, and mechanical properties in LBAM. Finally, our paper discusses the current study limitations and some future studies in LAM technology.

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Section: Microstructure and Mechanical Propertiesmentioning

confidence: 99%

Review on scanning pattern evaluation in laser-based additive manufacturing

et al. 2021

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“…The cellular automata (CA) method [9][10][11][12][13][14][15][16][17][18] and phase-field (PF) method [19][20][21][22][23] are two commonly used methods for simulating the microstructure evolution in AM processes. A relatively large number of CA simulations of grain evolution have been reported.…”

Section: Introductionmentioning

confidence: 99%

Phase-field modeling of grain evolutions in additive manufacturing from nucleation, growth, to coarsening

2021

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A three-dimensional phase-field model is developed to simulate grain evolutions during powder-bed-fusion (PBF) additive manufacturing, while the physically-informed temperature profile is implemented from a thermal-fluid flow model. The phase-field model incorporates a nucleation model based on classical nucleation theory, as well as the initial grain structures of powder particles and substrate. The grain evolutions during the three-layer three-track PBF process are comprehensively reproduced, including grain nucleation and growth in molten pools, epitaxial growth from powder particles, substrate and previous tracks, grain re-melting and re-growth in overlapping zones, and grain coarsening in heat-affected zones. A validation experiment has been carried out, showing that the simulation results are consistent with the experimental results in the molten pool and grain morphologies. Furthermore, the grain refinement by adding nanoparticles is preliminarily reproduced and compared against the experimental result in literature.

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“…The simulation can produce details about thermal history during printing [7,[14][15][16][17], and the computational results could be used for estimating the local microstructure, thermal deformations, and the optimization of the metal AM process. Several researchers coupled macroscale thermal simulation coupled with mesoscale microstructural evaluation [18][19][20][21]. There are several published kinds of research on mesoscale microstructural simulations for powder bed-based additive manufacturing in the first layer of the print [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Local Thermal Rates and Gradients in Laser Powder Bed Fusion Metal Additive Manufacturing Method: Computer Simulation

Hosseinzadeh¹

2021

Preprint

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The powder bed fusion (PBF) metal additive manufacturing (AM) method uses an energy source like a laser to melt the metal powders. The laser can locally melt the metal powders and creates a solid structure as it moves. The complexity of the heat distribution in laser PBF metal AM is one of the main features that need to be accurately addressed and understood to design and manage an optimized printing process. In this research, the dependency of local thermal rates and gradients on print after solidification (in the heat-affected zone) was numerically simulated and studied to provide information for designing the print process. The simulation results were validated by independent experimental results. The simulation shows that the local thermal rates are higher at higher laser power and scan speed. Also, the local thermal gradients increase if the laser power increases. The effect of scan speed on the thermal gradients is opposite during heating versus cooling times. Increasing the scan speed increases the local thermal gradients in the cooling times and decreases the local thermal gradients during the heating. In addition, these simulation results could be used in artificial intelligence (AI) and machine learning for developing digital additive manufacturing.

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Numerical methods for microstructural evolutions in laser additive manufacturing

Cited by 53 publications

References 47 publications

Review on scanning pattern evaluation in laser-based additive manufacturing

Review on scanning pattern evaluation in laser-based additive manufacturing

Phase-field modeling of grain evolutions in additive manufacturing from nucleation, growth, to coarsening

Local Thermal Rates and Gradients in Laser Powder Bed Fusion Metal Additive Manufacturing Method: Computer Simulation

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