Fast prediction of thermal distortion in metal powder bed fusion additive manufacturing: Part 2, a quasi-static thermo-mechanical model

Peng, Hao; Ghasri-Khouzani, Morteza; Gong, Shan; Attardo, Ross; Ostiguy, Pierre Claude; Rogge, R. B.; Gatrell, Bernice Aboud; Budzinski, Joseph; Tomonto, Charles; Neidig, Joel; Shankar, M. Ravi; Billo, Richard E.; Go, David B.; Hoelzle, David J.

doi:10.1016/j.addma.2018.05.001

Cited by 36 publications

(26 citation statements)

References 31 publications

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“…The finite element model in Ref. [21] involves comparable computational time as that for performing the full model transfer in this application, but its disadvantage is that it takes this amount of time for each new part. A comparison of the accuracies of Fig.…”

Section: Results Of the Transferred Model For 316l Stainlessmentioning

confidence: 99%

“…10 Posterior predictive means of distortions derived from our transferred model for 316L stainless steel disks of nominal diameters (a) 55 mm, (b) 65 mm, and (c) 70 mm distortion predictions between our model and that in Ref. [21] is not immediately available because the radius of curvature approach is used in Ref. [21] to fit a sphere to the distortion of the disk, whereas we directly use the pointwise distortion measurements obtained from the CMM.…”

Section: Results Of the Transferred Model For 316l Stainlessmentioning

confidence: 99%

“…[21] is not immediately available because the radius of curvature approach is used in Ref. [21] to fit a sphere to the distortion of the disk, whereas we directly use the pointwise distortion measurements obtained from the CMM. Model predictions given in Ref.…”

Section: Results Of the Transferred Model For 316l Stainlessmentioning

confidence: 99%

“…In the second part of Ref. [21], a quasi-static thermo-mechanical (QTM) model was introduced that can be used in tandem with the TCN to predict distortion for AlSi10Mg and 316L stainless steel disks. The TCN-QTM method was demonstrated to result in relative distortion prediction errors of 20% in terms of the radius of curvature for a wide range of test cases (e.g., disks, prisms, and triangular shapes).…”

Section: Finite Element Analysismentioning

confidence: 99%

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Efficient Distortion Prediction of Additively Manufactured Parts Using Bayesian Model Transfer Between Material Systems

Francis

Sabbaghi

Shankar

et al. 2020

Journal of Manufacturing Science and Engineering

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Distortion in laser-based additive manufacturing (LBAM) is a critical issue that adversely affects the geometric integrity of additively manufactured parts and generally exhibits a complicated dependence on the underlying material. The differences in properties between distinct materials prevent the immediate application of a distortion model learned for one material to another, which introduces the challenge in LBAM of learning a distortion model for a new material system given past experiments. Current methods for investigating the distortion of different material systems typically involve finite element analysis or a large number of experiments in an empirical study. However, these methods do not learn from previous experiments and can incur significant costs in terms of computation, time, or resources. We propose a Bayesian model transfer methodology that is both physics-based and data-driven to leverage past experiments on previously studied material systems for more efficient distortion modeling of new systems. This method transfers distortion models across distinct materials based on the statistical effect equivalence framework by formulating the differences between two materials as a lurking variable. Our method reduces the experimentation and effort needed for specifying distortion models for new material systems. We validate our methodology in a case study of distortion model transfer from Ti–6Al–4V disks to 316L stainless steel disks. This case study is the first instance of model transfer between material systems and illustrates the ability of the Bayesian model transfer methodology to address the issue of comprehensive distortion modeling across varying material systems in LBAM.

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Section: Results Of the Transferred Model For 316l Stainlessmentioning

confidence: 99%

Section: Results Of the Transferred Model For 316l Stainlessmentioning

confidence: 99%

Section: Results Of the Transferred Model For 316l Stainlessmentioning

confidence: 99%

Section: Finite Element Analysismentioning

confidence: 99%

See 2 more Smart Citations

Efficient Distortion Prediction of Additively Manufactured Parts Using Bayesian Model Transfer Between Material Systems

Francis

Sabbaghi

Shankar

et al. 2020

Journal of Manufacturing Science and Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are some experimental and computational investigations and papers discussed on thermal stress and deformation in additively manufactured metallic alloys [1,[31][32][33]. Thermal stress and deformation of additively manufactured are about several hundred MPa [34,35] and several micrometers for small samples [36][37][38][39], respectively. This level of deformation is high enough, especially for complex shapes to reject the printed sample for application.…”

Section: -Introductionmentioning

confidence: 99%

Direct Laser Deposition Metal 3D Printing at Different Temperatures of the Printer’s Chamber: Computer Simulation

Hosseinzadeh¹

2020

Preprint

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Metal 3D printing technology is a promising manufacturing method. The quality of the printed product can pass for mechanical application, if the anisotropy of the microstructure, imperfections, deformation, and residual stress of the printed sample could be lower than the appropriate level or if they are fully illuminated. Thermal stress is one of the significant reasons for deformation in the 3D printed samples. Thermal stresses are the direct consequence of the local temperature gradient. In this research, the effect of the temperature printer’s chamber (from room temperature to 900 C) was studied on thermal stress and subsequent total deformation in the printed sample. The printed sample is a six-layers-printed walk, which could be considered as a building block of other complex shapes and give us inside about deformation. The computational results show a meaningful reduction in thermal stress and deformation at the higher temperature of the printer’s chamber. The lower final deformation of the printed sample is an important subject, especially for samples with complex shapes.

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Modified Inherent Strain Method for Predicting Residual Deformation and Stress in Metal Additive Manufacturing

Liang

Dong

2023

Additive Manufacturing Technology

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Fast prediction of thermal distortion in metal powder bed fusion additive manufacturing: Part 2, a quasi-static thermo-mechanical model

Cited by 36 publications

References 31 publications

Efficient Distortion Prediction of Additively Manufactured Parts Using Bayesian Model Transfer Between Material Systems

Efficient Distortion Prediction of Additively Manufactured Parts Using Bayesian Model Transfer Between Material Systems

Direct Laser Deposition Metal 3D Printing at Different Temperatures of the Printer’s Chamber: Computer Simulation

Modified Inherent Strain Method for Predicting Residual Deformation and Stress in Metal Additive Manufacturing

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