Deep-learned generators of porosity distributions produced during metal Additive Manufacturing
Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. However, AM produced parts can be subject to undesirable porosity, negatively influencing the properties of printed componen...
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| Published in | Additive manufacturing Vol. 60; no. PA; p. 103250 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Netherlands
Elsevier B.V
01.12.2022
Elsevier |
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| Abstract | Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. However, AM produced parts can be subject to undesirable porosity, negatively influencing the properties of printed components. Thus, controlling porosity is integral for creating effective parts. A precise understanding of the porosity distribution is crucial for accurately simulating potential fatigue and failure zones. Previous research on generating synthetic porous microstructures have succeeded in generating parts with high density, isotropic porosity distributions but are often inapplicable to cases with sparser, boundary-dependent pore distributions. Our work bridges this gap by providing a method that considers these constraints by deconstructing the generation problem into its constitutive parts. A framework is introduced that combines Generative Adversarial Networks with Mallat Scattering Transform-based autocorrelation methods to construct novel realizations of the individual pore geometries and surface roughness, then stochastically reconstruct them to form realizations of a porous printed part. The generated parts are compared to the existing experimental porosity distributions based on statistical and dimensional metrics, such as nearest neighbor distances, pore volumes, pore anisotropies and scattering transform based auto-correlations. |
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| AbstractList | Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. However, AM produced parts can be subject to undesirable porosity, negatively influencing the properties of printed components. Thus, controlling porosity is integral for creating effective parts. A precise understanding of the porosity distribution is crucial for accurately simulating potential fatigue and failure zones. Previous research on generating synthetic porous microstructures have succeeded in generating parts with high density, isotropic porosity distributions but are often inapplicable to cases with sparser, boundary-dependent pore distributions. Our work bridges this gap by providing a method that considers these constraints by deconstructing the generation problem into its constitutive parts. A framework is introduced that combines Generative Adversarial Networks with Mallat Scattering Transform-based autocorrelation methods to construct novel realizations of the individual pore geometries and surface roughness, then stochastically reconstruct them to form realizations of a porous printed part. The generated parts are compared to the existing experimental porosity distributions based on statistical and dimensional metrics, such as nearest neighbor distances, pore volumes, pore anisotropies and scattering transform based auto-correlations. |
| ArticleNumber | 103250 |
| Author | Barati Farimani, Amir Glinsky, Michael Kramer, Sharlotte Laursen, Chris Johnson, Kyle Ogoke, Odinakachukwu Francis |
| Author_xml | – sequence: 1 givenname: Odinakachukwu Francis orcidid: 0000-0002-2432-7783 surname: Ogoke fullname: Ogoke, Odinakachukwu Francis organization: Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA – sequence: 2 givenname: Kyle surname: Johnson fullname: Johnson, Kyle organization: Sandia National Laboratories, 1515 Eubank Avenue, Albuquerque, 87185, NM, USA – sequence: 3 givenname: Michael surname: Glinsky fullname: Glinsky, Michael organization: Sandia National Laboratories, 1515 Eubank Avenue, Albuquerque, 87185, NM, USA – sequence: 4 givenname: Chris surname: Laursen fullname: Laursen, Chris organization: Sandia National Laboratories, 1515 Eubank Avenue, Albuquerque, 87185, NM, USA – sequence: 5 givenname: Sharlotte orcidid: 0000-0001-6015-8385 surname: Kramer fullname: Kramer, Sharlotte organization: Sandia National Laboratories, 1515 Eubank Avenue, Albuquerque, 87185, NM, USA – sequence: 6 givenname: Amir orcidid: 0000-0002-2952-8576 surname: Barati Farimani fullname: Barati Farimani, Amir email: barati@cmu.edu organization: Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA |
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| Cites_doi | 10.3847/1538-4357/abe46d 10.1038/srep15880 10.1007/s00339-019-3092-9 10.1016/j.msea.2019.04.101 10.1093/mnras/staa3165 10.1016/j.msea.2019.138525 10.1007/s11661-010-0397-x 10.1016/j.commatsci.2017.11.015 10.1016/0021-9797(84)90481-8 10.1016/S1005-0302(12)60016-4 10.1146/annurev-matsci-070115-032024 10.1038/s41524-020-0340-7 10.3390/ma13061397 10.1016/j.matdes.2015.07.147 10.1016/j.petrol.2013.10.005 10.1016/j.petrol.2019.106794 10.1063/5.0010781 10.1002/cpa.21413 10.1016/j.commatsci.2015.09.015 10.1146/annurev-matsci-070115-032158 10.1108/13552541311302932 10.1016/j.acha.2021.01.003 10.1021/ac60214a047 10.1080/21663831.2017.1340911 10.1002/admt.201900981 10.1016/j.optlastec.2018.08.042 10.1103/PhysRevE.98.043310 10.1016/j.msea.2015.10.068 10.1016/j.earscirev.2013.04.003 10.1016/j.matdes.2017.11.021 10.1016/j.ijmecsci.2020.105428 10.1103/PhysRevE.57.495 10.1103/PhysRevE.96.043309 10.1016/j.jpowsour.2012.05.121 |
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| Keywords | Deep learning 0000 1111 Microstructural analysis Generative Adversarial Networks Additive Manufacturing Porosity |
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| References | Mallat (b39) 2012; 65 Mosser, Dubrule, Blunt (b30) 2017; 96 Moran, Warner, Soltani-Tehrani, Shamsaei, Phan (b45) 2021; 47 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (b27) 2014; 27 Mower, Long (b4) 2016; 651 Moussaoui, Laurencin, Gavet, Delette, Hubert, Cloetens, Le Bihan, Debayle (b24) 2018; 143 Mirza, Osindero (b28) 2014 Tang, Pistorius, Beuth (b15) 2017; 14 Glinsky, Moore, Lewis, Weis, Jennings, Ampleford, Knapp, Harding, Gomez, Harvey-Thompson (b37) 2020; 27 Arjovsky, Chintala, Bottou (b46) 2017 Gong, Rafi, Gu, Janaki Ram, Starr, Stucker (b14) 2015; 86 Al-Bermani, Blackmore, Zhang, Todd (b10) 2010; 41 Vilardell, Fredriksson, Yadroitsev, Krakhmalev (b12) 2019; 109 J. Wu, C. Zhang, T. Xue, W.T. Freeman, J.B. Tenenbaum, Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling, in: Proceedings of the 30th International Conference on Neural Information Processing Systems, 2016, pp. 82–90. Kok, Tan, Wang, Nai, Loh, Liu, Tor (b7) 2018; 139 Salarian, Asgari, Vlasea (b42) 2020; 769 Murr, Gaytan, Ramirez, Martinez, Hernandez, Amato, Shindo, Medina, Wicker (b9) 2012; 28 Wang, Arns, Rahman, Arns (b25) 2018; 98 Hasanabadi, Baniassadi, Abrinia, Safdari, Garmestani (b21) 2016; 111 Gerke, Karsanina, Mallants (b22) 2015; 5 Shams, Masihi, Boozarjomehry, Blunt (b33) 2020; 186 Li, Feng, Hao, Huang, Xin, Wang, Bilotti, Essa, Zhang, Li (b1) 2020; 5 Zhu, Lou, Majewski (b47) 2020; 36 Janssens, Huysmans, Swennen (b32) 2020; 13 Mallat, Zhang, Rochette (b40) 2018 Siddique, Salehi, Liu (b18) 2012; 217 Reijonen, Revuelta, Riipinen, Ruusuvuori, Puukko (b44) 2020; 32 Andreau, Pessard, Koutiri, Penot, Dupuy, Saintier, Peyre (b13) 2019; 757 Savitzky, Golay (b41) 1964; 36 Cunningham, Nicolas, Madsen, Fodran, Anagnostou, Sangid, Rollett (b3) 2017; 5 Saydjari, Portillo, Slepian, Kahraman, Burkhart, Finkbeiner (b35) 2021; 910 Yao, Wang, Yang, Hu, Wang (b26) 2013; 110 Markl, Körner (b11) 2016; 46 Lewandowski, Seifi (b6) 2016; 46 Farimani, Gomes, Pande (b29) 2017 A. Gayon-Lombardo, L. Mosser, N.P. Brandon, S.J. Cooper, ARTICLE Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries Reeves, Tuck, Hague (b2) 2011 Yeong, Torquato (b23) 1998; 57 . Spierings, Starr, Wegener (b5) 2013 Ning, Sievers, Garmestani, Liang (b17) 2020; 172 Li, Yang, Brinson, Choudhary, Agrawal, Chen (b34) 2018 Quiblier (b19) 1984; 98 Zhang, Mallat (b38) 2021; 53 Ning, Wang, Zamorano, Liang (b16) 2019; 125 Cnudde, Boone (b20) 2013; 123 Mani, Feng, Lane, Donmez, Moylan, Fesperman (b8) 2015 Cheng, Ting, Ménard, Bruna (b36) 2020; 499 Mallat (10.1016/j.addma.2022.103250_b39) 2012; 65 Mower (10.1016/j.addma.2022.103250_b4) 2016; 651 Cheng (10.1016/j.addma.2022.103250_b36) 2020; 499 Salarian (10.1016/j.addma.2022.103250_b42) 2020; 769 Vilardell (10.1016/j.addma.2022.103250_b12) 2019; 109 Ning (10.1016/j.addma.2022.103250_b17) 2020; 172 Hasanabadi (10.1016/j.addma.2022.103250_b21) 2016; 111 10.1016/j.addma.2022.103250_b43 Spierings (10.1016/j.addma.2022.103250_b5) 2013 Zhang (10.1016/j.addma.2022.103250_b38) 2021; 53 Siddique (10.1016/j.addma.2022.103250_b18) 2012; 217 Moran (10.1016/j.addma.2022.103250_b45) 2021; 47 Shams (10.1016/j.addma.2022.103250_b33) 2020; 186 Tang (10.1016/j.addma.2022.103250_b15) 2017; 14 Saydjari (10.1016/j.addma.2022.103250_b35) 2021; 910 Ning (10.1016/j.addma.2022.103250_b16) 2019; 125 Arjovsky (10.1016/j.addma.2022.103250_b46) 2017 Janssens (10.1016/j.addma.2022.103250_b32) 2020; 13 Cnudde (10.1016/j.addma.2022.103250_b20) 2013; 123 Gerke (10.1016/j.addma.2022.103250_b22) 2015; 5 Andreau (10.1016/j.addma.2022.103250_b13) 2019; 757 Quiblier (10.1016/j.addma.2022.103250_b19) 1984; 98 Cunningham (10.1016/j.addma.2022.103250_b3) 2017; 5 Yeong (10.1016/j.addma.2022.103250_b23) 1998; 57 Zhu (10.1016/j.addma.2022.103250_b47) 2020; 36 Reeves (10.1016/j.addma.2022.103250_b2) 2011 Lewandowski (10.1016/j.addma.2022.103250_b6) 2016; 46 Kok (10.1016/j.addma.2022.103250_b7) 2018; 139 Wang (10.1016/j.addma.2022.103250_b25) 2018; 98 Markl (10.1016/j.addma.2022.103250_b11) 2016; 46 Mirza (10.1016/j.addma.2022.103250_b28) 2014 10.1016/j.addma.2022.103250_b31 Glinsky (10.1016/j.addma.2022.103250_b37) 2020; 27 Mosser (10.1016/j.addma.2022.103250_b30) 2017; 96 Al-Bermani (10.1016/j.addma.2022.103250_b10) 2010; 41 Moussaoui (10.1016/j.addma.2022.103250_b24) 2018; 143 Li (10.1016/j.addma.2022.103250_b34) 2018 Goodfellow (10.1016/j.addma.2022.103250_b27) 2014; 27 Yao (10.1016/j.addma.2022.103250_b26) 2013; 110 Mallat (10.1016/j.addma.2022.103250_b40) 2018 Savitzky (10.1016/j.addma.2022.103250_b41) 1964; 36 Farimani (10.1016/j.addma.2022.103250_b29) 2017 Mani (10.1016/j.addma.2022.103250_b8) 2015 Gong (10.1016/j.addma.2022.103250_b14) 2015; 86 Li (10.1016/j.addma.2022.103250_b1) 2020; 5 Murr (10.1016/j.addma.2022.103250_b9) 2012; 28 Reijonen (10.1016/j.addma.2022.103250_b44) 2020; 32 |
| References_xml | – volume: 143 start-page: 262 year: 2018 end-page: 276 ident: b24 article-title: Stochastic geometrical modeling of solid oxide cells electrodes validated on 3D reconstructions publication-title: Comput. Mater. Sci. – volume: 5 start-page: 1 year: 2015 end-page: 12 ident: b22 article-title: Universal stochastic multiscale image fusion: An example application for shale rock publication-title: Sci. Rep. – volume: 651 start-page: 198 year: 2016 end-page: 213 ident: b4 article-title: Mechanical behavior of additive manufactured, powder-bed laser-fused materials publication-title: Mater. Sci. Eng. A – volume: 5 start-page: 516 year: 2017 end-page: 525 ident: b3 article-title: Analyzing the effects of powder and post-processing on porosity and properties of electron beam melted Ti-6Al-4V publication-title: Mater. Res. Lett. – volume: 53 start-page: 199 year: 2021 end-page: 230 ident: b38 article-title: Maximum entropy models from phase harmonic covariances publication-title: Appl. Comput. Harmon. Anal. – volume: 36 start-page: 1627 year: 1964 end-page: 1639 ident: b41 article-title: Smoothing and differentiation of data by simplified least squares procedures. publication-title: Anal. Chem. – volume: 5 year: 2020 ident: b1 article-title: A review on functionally graded materials and structures via additive manufacturing: from multi-scale design to versatile functional properties publication-title: Adv. Mater. Technol. – volume: 910 start-page: 122 year: 2021 ident: b35 article-title: Classification of magnetohydrodynamic simulations using wavelet scattering transforms publication-title: Astrophys. J. – year: 2013 ident: b5 article-title: Fatigue performance of additive manufactured metallic parts publication-title: Rapid Prototyp. J. – year: 2015 ident: b8 article-title: Measurement science needs for real-time control of additive manufacturing powder bed fusion processes – volume: 186 year: 2020 ident: b33 article-title: Coupled generative adversarial and auto-encoder neural networks to reconstruct three-dimensional multi-scale porous media publication-title: J. Pet. Sci. Eng. – volume: 86 start-page: 545 year: 2015 end-page: 554 ident: b14 article-title: Influence of defects on mechanical properties of Ti–6Al–4 V components produced by selective laser melting and electron beam melting publication-title: Mater. Des. – year: 2017 ident: b29 article-title: Deep learning the physics of transport phenomena – volume: 47 year: 2021 ident: b45 article-title: Spatial inhomogeneity of build defects across the build plate in laser powder bed fusion publication-title: Addit. Manuf. – volume: 96 year: 2017 ident: b30 article-title: Reconstruction of three-dimensional porous media using generative adversarial neural networks publication-title: Phys. Rev. E – start-page: 214 year: 2017 end-page: 223 ident: b46 article-title: Wasserstein generative adversarial networks publication-title: International Conference on Machine Learning – volume: 57 start-page: 495 year: 1998 ident: b23 article-title: Reconstructing random media publication-title: Phys. Rev. E – reference: J. Wu, C. Zhang, T. Xue, W.T. Freeman, J.B. Tenenbaum, Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling, in: Proceedings of the 30th International Conference on Neural Information Processing Systems, 2016, pp. 82–90. – volume: 98 year: 2018 ident: b25 article-title: Three-dimensional porous structure reconstruction based on structural local similarity via sparse representation on micro-computed-tomography images publication-title: Phys. Rev. E – volume: 125 start-page: 1 year: 2019 end-page: 11 ident: b16 article-title: Analytical modeling of lack-of-fusion porosity in metal additive manufacturing publication-title: Appl. Phys. A – volume: 14 start-page: 39 year: 2017 end-page: 48 ident: b15 article-title: Prediction of lack-of-fusion porosity for powder bed fusion publication-title: Addit. Manuf. – volume: 110 start-page: 263 year: 2013 end-page: 267 ident: b26 article-title: The construction of carbonate digital rock with hybrid superposition method publication-title: J. Pet. Sci. Eng. – volume: 46 start-page: 93 year: 2016 end-page: 123 ident: b11 article-title: Multiscale modeling of powder bed–based additive manufacturing publication-title: Annu. Rev. Mater. Res. – year: 2014 ident: b28 article-title: Conditional generative adversarial nets – volume: 13 start-page: 1397 year: 2020 ident: b32 article-title: Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow publication-title: Materials – volume: 98 start-page: 84 year: 1984 end-page: 102 ident: b19 article-title: A new three-dimensional modeling technique for studying porous media publication-title: J. Colloid Interface Sci. – volume: 123 start-page: 1 year: 2013 end-page: 17 ident: b20 article-title: High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications publication-title: Earth-Sci. Rev. – volume: 27 year: 2020 ident: b37 article-title: Quantification of MagLIF morphology using the Mallat scattering transformation publication-title: Phys. Plasmas – volume: 65 start-page: 1331 year: 2012 end-page: 1398 ident: b39 article-title: Group invariant scattering publication-title: Comm. Pure Appl. Math. – year: 2018 ident: b40 article-title: Phase harmonics and correlation invariants in convolutional neural networks – volume: 769 year: 2020 ident: b42 article-title: Pore space characteristics and corresponding effect on tensile properties of inconel 625 fabricated via laser powder bed fusion publication-title: Mater. Sci. Eng. A – volume: 32 year: 2020 ident: b44 article-title: On the effect of shielding gas flow on porosity and melt pool geometry in laser powder bed fusion additive manufacturing publication-title: Addit. Manuf. – start-page: 275 year: 2011 end-page: 289 ident: b2 article-title: Additive manufacturing for mass customization publication-title: Mass Customization – volume: 172 year: 2020 ident: b17 article-title: Analytical modeling of part porosity in metal additive manufacturing publication-title: Int. J. Mech. Sci. – reference: A. Gayon-Lombardo, L. Mosser, N.P. Brandon, S.J. Cooper, ARTICLE Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries, – reference: . – volume: 139 start-page: 565 year: 2018 end-page: 586 ident: b7 article-title: Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: A critical review publication-title: Mater. Des. – volume: 41 start-page: 3422 year: 2010 end-page: 3434 ident: b10 article-title: The origin of microstructural diversity, texture, and mechanical properties in electron beam melted Ti-6Al-4V publication-title: Metall. Mater. Trans. A – volume: 27 year: 2014 ident: b27 article-title: Generative adversarial nets publication-title: Adv. Neural Inf. Process. Syst. – volume: 28 start-page: 1 year: 2012 end-page: 14 ident: b9 article-title: Metal fabrication by additive manufacturing using laser and electron beam melting technologies publication-title: J. Mater. Sci. Technol. – volume: 217 start-page: 437 year: 2012 end-page: 443 ident: b18 article-title: Stochastic reconstruction and electrical transport studies of porous cathode of Li-ion batteries publication-title: J. Power Sources – volume: 109 start-page: 608 year: 2019 end-page: 615 ident: b12 article-title: Fracture mechanisms in the as-built and stress-relieved laser powder bed fusion Ti6Al4V ELI alloy publication-title: Opt. Laser Technol. – volume: 111 start-page: 107 year: 2016 end-page: 115 ident: b21 article-title: 3D microstructural reconstruction of heterogeneous materials from 2D cross sections: A modified phase-recovery algorithm publication-title: Comput. Mater. Sci. – volume: 36 year: 2020 ident: b47 article-title: Characterisation and correlation of areal surface texture with processing parameters and porosity of high speed sintered parts publication-title: Addit. Manuf. – volume: 46 year: 2016 ident: b6 article-title: Metal additive manufacturing: a review of mechanical properties publication-title: Annu. Rev. Mater. Res. – year: 2018 ident: b34 article-title: A Deep Adversarial Learning Methodology for Designing Microstructural Material Systems – volume: 499 start-page: 5902 year: 2020 end-page: 5914 ident: b36 article-title: A new approach to observational cosmology using the scattering transform publication-title: Mon. Not. R. Astron. Soc. – volume: 757 start-page: 146 year: 2019 end-page: 159 ident: b13 article-title: A competition between the contour and hatching zones on the high cycle fatigue behaviour of a 316L stainless steel: Analyzed using X-ray computed tomography publication-title: Mater. Sci. Eng. A – year: 2014 ident: 10.1016/j.addma.2022.103250_b28 – volume: 910 start-page: 122 issue: 2 year: 2021 ident: 10.1016/j.addma.2022.103250_b35 article-title: Classification of magnetohydrodynamic simulations using wavelet scattering transforms publication-title: Astrophys. J. doi: 10.3847/1538-4357/abe46d – volume: 5 start-page: 1 issue: 1 year: 2015 ident: 10.1016/j.addma.2022.103250_b22 article-title: Universal stochastic multiscale image fusion: An example application for shale rock publication-title: Sci. Rep. doi: 10.1038/srep15880 – volume: 125 start-page: 1 issue: 11 year: 2019 ident: 10.1016/j.addma.2022.103250_b16 article-title: Analytical modeling of lack-of-fusion porosity in metal additive manufacturing publication-title: Appl. Phys. A doi: 10.1007/s00339-019-3092-9 – volume: 757 start-page: 146 year: 2019 ident: 10.1016/j.addma.2022.103250_b13 article-title: A competition between the contour and hatching zones on the high cycle fatigue behaviour of a 316L stainless steel: Analyzed using X-ray computed tomography publication-title: Mater. Sci. Eng. A doi: 10.1016/j.msea.2019.04.101 – year: 2017 ident: 10.1016/j.addma.2022.103250_b29 – volume: 499 start-page: 5902 issue: 4 year: 2020 ident: 10.1016/j.addma.2022.103250_b36 article-title: A new approach to observational cosmology using the scattering transform publication-title: Mon. Not. R. Astron. Soc. doi: 10.1093/mnras/staa3165 – volume: 769 year: 2020 ident: 10.1016/j.addma.2022.103250_b42 article-title: Pore space characteristics and corresponding effect on tensile properties of inconel 625 fabricated via laser powder bed fusion publication-title: Mater. Sci. Eng. A doi: 10.1016/j.msea.2019.138525 – volume: 41 start-page: 3422 issue: 13 year: 2010 ident: 10.1016/j.addma.2022.103250_b10 article-title: The origin of microstructural diversity, texture, and mechanical properties in electron beam melted Ti-6Al-4V publication-title: Metall. Mater. Trans. A doi: 10.1007/s11661-010-0397-x – volume: 143 start-page: 262 year: 2018 ident: 10.1016/j.addma.2022.103250_b24 article-title: Stochastic geometrical modeling of solid oxide cells electrodes validated on 3D reconstructions publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2017.11.015 – volume: 32 year: 2020 ident: 10.1016/j.addma.2022.103250_b44 article-title: On the effect of shielding gas flow on porosity and melt pool geometry in laser powder bed fusion additive manufacturing publication-title: Addit. Manuf. – volume: 98 start-page: 84 issue: 1 year: 1984 ident: 10.1016/j.addma.2022.103250_b19 article-title: A new three-dimensional modeling technique for studying porous media publication-title: J. Colloid Interface Sci. doi: 10.1016/0021-9797(84)90481-8 – volume: 28 start-page: 1 issue: 1 year: 2012 ident: 10.1016/j.addma.2022.103250_b9 article-title: Metal fabrication by additive manufacturing using laser and electron beam melting technologies publication-title: J. Mater. Sci. Technol. doi: 10.1016/S1005-0302(12)60016-4 – volume: 46 year: 2016 ident: 10.1016/j.addma.2022.103250_b6 article-title: Metal additive manufacturing: a review of mechanical properties publication-title: Annu. Rev. Mater. Res. doi: 10.1146/annurev-matsci-070115-032024 – year: 2018 ident: 10.1016/j.addma.2022.103250_b40 – ident: 10.1016/j.addma.2022.103250_b31 doi: 10.1038/s41524-020-0340-7 – volume: 47 year: 2021 ident: 10.1016/j.addma.2022.103250_b45 article-title: Spatial inhomogeneity of build defects across the build plate in laser powder bed fusion publication-title: Addit. Manuf. – year: 2018 ident: 10.1016/j.addma.2022.103250_b34 – start-page: 275 year: 2011 ident: 10.1016/j.addma.2022.103250_b2 article-title: Additive manufacturing for mass customization – year: 2015 ident: 10.1016/j.addma.2022.103250_b8 – volume: 14 start-page: 39 year: 2017 ident: 10.1016/j.addma.2022.103250_b15 article-title: Prediction of lack-of-fusion porosity for powder bed fusion publication-title: Addit. Manuf. – volume: 13 start-page: 1397 issue: 6 year: 2020 ident: 10.1016/j.addma.2022.103250_b32 article-title: Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow publication-title: Materials doi: 10.3390/ma13061397 – volume: 86 start-page: 545 year: 2015 ident: 10.1016/j.addma.2022.103250_b14 article-title: Influence of defects on mechanical properties of Ti–6Al–4 V components produced by selective laser melting and electron beam melting publication-title: Mater. Des. doi: 10.1016/j.matdes.2015.07.147 – volume: 110 start-page: 263 year: 2013 ident: 10.1016/j.addma.2022.103250_b26 article-title: The construction of carbonate digital rock with hybrid superposition method publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2013.10.005 – volume: 186 year: 2020 ident: 10.1016/j.addma.2022.103250_b33 article-title: Coupled generative adversarial and auto-encoder neural networks to reconstruct three-dimensional multi-scale porous media publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2019.106794 – volume: 27 issue: 11 year: 2020 ident: 10.1016/j.addma.2022.103250_b37 article-title: Quantification of MagLIF morphology using the Mallat scattering transformation publication-title: Phys. Plasmas doi: 10.1063/5.0010781 – volume: 65 start-page: 1331 issue: 10 year: 2012 ident: 10.1016/j.addma.2022.103250_b39 article-title: Group invariant scattering publication-title: Comm. Pure Appl. Math. doi: 10.1002/cpa.21413 – volume: 111 start-page: 107 year: 2016 ident: 10.1016/j.addma.2022.103250_b21 article-title: 3D microstructural reconstruction of heterogeneous materials from 2D cross sections: A modified phase-recovery algorithm publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2015.09.015 – volume: 46 start-page: 93 year: 2016 ident: 10.1016/j.addma.2022.103250_b11 article-title: Multiscale modeling of powder bed–based additive manufacturing publication-title: Annu. Rev. Mater. Res. doi: 10.1146/annurev-matsci-070115-032158 – ident: 10.1016/j.addma.2022.103250_b43 – year: 2013 ident: 10.1016/j.addma.2022.103250_b5 article-title: Fatigue performance of additive manufactured metallic parts publication-title: Rapid Prototyp. J. doi: 10.1108/13552541311302932 – volume: 53 start-page: 199 year: 2021 ident: 10.1016/j.addma.2022.103250_b38 article-title: Maximum entropy models from phase harmonic covariances publication-title: Appl. Comput. Harmon. Anal. doi: 10.1016/j.acha.2021.01.003 – volume: 36 start-page: 1627 issue: 8 year: 1964 ident: 10.1016/j.addma.2022.103250_b41 article-title: Smoothing and differentiation of data by simplified least squares procedures. publication-title: Anal. Chem. doi: 10.1021/ac60214a047 – volume: 5 start-page: 516 issue: 7 year: 2017 ident: 10.1016/j.addma.2022.103250_b3 article-title: Analyzing the effects of powder and post-processing on porosity and properties of electron beam melted Ti-6Al-4V publication-title: Mater. Res. Lett. doi: 10.1080/21663831.2017.1340911 – start-page: 214 year: 2017 ident: 10.1016/j.addma.2022.103250_b46 article-title: Wasserstein generative adversarial networks – volume: 5 issue: 6 year: 2020 ident: 10.1016/j.addma.2022.103250_b1 article-title: A review on functionally graded materials and structures via additive manufacturing: from multi-scale design to versatile functional properties publication-title: Adv. Mater. Technol. doi: 10.1002/admt.201900981 – volume: 109 start-page: 608 year: 2019 ident: 10.1016/j.addma.2022.103250_b12 article-title: Fracture mechanisms in the as-built and stress-relieved laser powder bed fusion Ti6Al4V ELI alloy publication-title: Opt. Laser Technol. doi: 10.1016/j.optlastec.2018.08.042 – volume: 98 issue: 4 year: 2018 ident: 10.1016/j.addma.2022.103250_b25 article-title: Three-dimensional porous structure reconstruction based on structural local similarity via sparse representation on micro-computed-tomography images publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.98.043310 – volume: 27 year: 2014 ident: 10.1016/j.addma.2022.103250_b27 article-title: Generative adversarial nets publication-title: Adv. Neural Inf. Process. Syst. – volume: 651 start-page: 198 year: 2016 ident: 10.1016/j.addma.2022.103250_b4 article-title: Mechanical behavior of additive manufactured, powder-bed laser-fused materials publication-title: Mater. Sci. Eng. A doi: 10.1016/j.msea.2015.10.068 – volume: 123 start-page: 1 year: 2013 ident: 10.1016/j.addma.2022.103250_b20 article-title: High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications publication-title: Earth-Sci. Rev. doi: 10.1016/j.earscirev.2013.04.003 – volume: 139 start-page: 565 year: 2018 ident: 10.1016/j.addma.2022.103250_b7 article-title: Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: A critical review publication-title: Mater. Des. doi: 10.1016/j.matdes.2017.11.021 – volume: 172 year: 2020 ident: 10.1016/j.addma.2022.103250_b17 article-title: Analytical modeling of part porosity in metal additive manufacturing publication-title: Int. J. Mech. Sci. doi: 10.1016/j.ijmecsci.2020.105428 – volume: 36 year: 2020 ident: 10.1016/j.addma.2022.103250_b47 article-title: Characterisation and correlation of areal surface texture with processing parameters and porosity of high speed sintered parts publication-title: Addit. Manuf. – volume: 57 start-page: 495 issue: 1 year: 1998 ident: 10.1016/j.addma.2022.103250_b23 article-title: Reconstructing random media publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.57.495 – volume: 96 issue: 4 year: 2017 ident: 10.1016/j.addma.2022.103250_b30 article-title: Reconstruction of three-dimensional porous media using generative adversarial neural networks publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.96.043309 – volume: 217 start-page: 437 year: 2012 ident: 10.1016/j.addma.2022.103250_b18 article-title: Stochastic reconstruction and electrical transport studies of porous cathode of Li-ion batteries publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2012.05.121 |
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| Title | Deep-learned generators of porosity distributions produced during metal Additive Manufacturing |
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