Analytical estimation of fusion zone dimensions and cooling rates in part scale laser powder bed fusion

Zagade, Pramod; Gautham, B. P.; De, A.; DebRoy, T.

doi:10.1016/j.addma.2021.102222

Cited by 12 publications

(11 citation statements)

References 43 publications

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“…The complex melt flow conduces towards the creation of spherical pores, as illustrated in Figure 12. The two distinct melt pool regimes (based on the choice of process parameters) discussed above are termed conduction-mode [44] and keyhole-mode regime [99] in PBF processes.…”

Section: High-fidelity Multiphysics Modelsmentioning

confidence: 99%

Multiphysics multi-scale computational framework for linking process–structure–property relationships in metal additive manufacturing: a critical review

Sharma

Joshi

Pantawane

et al. 2023

International Materials Reviews

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This review article provides a critical assessment of the progress made in computational modelling of metal-based additive manufacturing (AM) with emphasis on its ability to predict physical phenomena, concepts of microstructural evolution, residual stresses, role of multiple thermal cycles, and formation of multi-dimensional defects along with the achieved degree of experimental validation. The uniqueness of this article stems from the inclusion of comprehensive information on computational progress in the field of fusion-based, sintering-based, and mechanical deformation-based AM. A computational model's role in determining the process framework for the desired outcome of the set properties of the AM components is recognised while presenting the process-microstructure maps, thereby appraising computational ability towards the qualification of products. The inclusion of a detailed discussion on the bi-directional coupling of machine learning and physics-based computational models provides a futuristic roadmap for the digital twin of metal-based AM.

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Section: High-fidelity Multiphysics Modelsmentioning

confidence: 99%

Multiphysics multi-scale computational framework for linking process–structure–property relationships in metal additive manufacturing: a critical review

Sharma

Joshi

Pantawane

et al. 2023

International Materials Reviews

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“…The term π 1 is considered equal to the measured porosity fraction and the terms π 2 , π 3 , and π 4 are calculated considering the corresponding LPBF conditions from the literature. An analytical heat conduction model [19] is used to compute the melt pool dimensions w and d, which are required to calculate π 2 and π 3 , respectively. Table 2 provides the range of LPBF conditions and material properties of the alloys, which are used here.…”

Section: Dimensional Analysismentioning

confidence: 99%

“…The values of the porosity fraction are almost Table 2. Ranges of process conditions and measured porosity fraction values for LPBF of Ti6Al4V [4,20,24], SS316L [22,23,25,26], AlSi10Mg [5,27], H13 [28,29], and IN718 [6,21,30] powder and material properties [31][32][33] used for the analytical model [19]. Conductivity, k [20,24], IN718 [6,21], SS316L [25,26], AlSi10Mg [27], and H13 [28,29].…”

Section: Analysis Of Dimensionless Variablesmentioning

confidence: 99%

“…The correlations among the dimensionless terms π 2 , π 3 , and π 4 are examined first by calculating the Pearson correlation coefficients [34,35] for one hundred seventy sets of LPBF conditions, the corresponding calculated melt pool dimensions using an analytical model [19], and the respective measured values of the porosity fractions from literature. Figure 4(a) shows a moderately positive correlation between the terms π 2 and π 3 , which is attributed to the similar nature of variations in these two terms for the range of LPBF conditions.…”

Section: Development Of Functional Relationshipmentioning

confidence: 99%

“…Here, we propose a novel scaling analysis to identify a set of non-dimensional terms to identify the susceptibility of an LPBF condition to lack-of-fusion porosity. These non-dimensional terms involve important LPBF variables, alloy properties, and melt pool dimensions, which are computed from a well-established analytical heat conduction model [19]. The sensitivity of the lack-of-fusion porosity to the non-dimensional terms is tested using measured porosity fraction data from the literature for LPBF of five commonly used powders.…”

Section: Introductionmentioning

confidence: 99%

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Scaling analysis for rapid estimation of lack of fusion porosity in laser powder bed fusion

Zagade

Gautham

et al. 2023

Science and Technology of Welding and Joining

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Lack-of-fusion porosity due to insufficient melting of the adjacent tracks and successive layers is a persistent problem in laser powder bed fusion (LPBF). A novel functional relation is proposed here following dimensional analysis for rapid estimation of the lack-of-fusion porosity fraction as a function of important process variables and alloy properties. The estimated values of the porosity fractions are found to be in fair agreement with the corresponding independent experimentally measured results for LPBF of five commonly used alloys for a wide range of process conditions. The proposed functional relation is used further to construct a set of process maps that can aid in the selection of important LPBF conditions to avoid lack-of-fusion porosity on the shop floor without doing any calculations.

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Laser Matter Interaction in LAM

2022

Laser‐Based Additive Manufacturing

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Analytical estimation of fusion zone dimensions and cooling rates in part scale laser powder bed fusion

Cited by 12 publications

References 43 publications

Multiphysics multi-scale computational framework for linking process–structure–property relationships in metal additive manufacturing: a critical review

Multiphysics multi-scale computational framework for linking process–structure–property relationships in metal additive manufacturing: a critical review

Scaling analysis for rapid estimation of lack of fusion porosity in laser powder bed fusion

Laser Matter Interaction in LAM

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