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'''Wing Kam Liu<sup>1</sup>, Puijei Cheng<sup>1</sup>, Orion L. Kafka<sup>1</sup>, Wei Xiong<sup>2</sup>, Zeliang Liu<sup>1</sup>, Wentao Yan<sup>1,3</sup>, and Jacob Smith<sup>1</sup>'''
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==Abstract==
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Additive manufacturing (AM) processes have the ability to build complex geometries from a wide variety of materials. A popular approach for metal-based AM processes involves the deposition of material particles on a substrate followed by fusion of those particles together using a high intensity heat source, e.g. a laser or an electron beam [<span id='cite-1'></span>[[#1|1]]], in order to fabricate a solid part. These methods are of high priority in engineering research, especially in applications for the energy, health, and defense sectors. The primary reasons behind the rapid growth in interest for AM include: (1) the ability to create complex geometries which are otherwise cost-prohibitive or difficult to manufacture, (2) increased freedom of material composition design through the adjustment of the ratios of the composing powders, (3) a reduction in wasted materials, and (4) the fast, low-volume, production of prototype and functional parts without the additional tooling and die requirements of conventional manufacturing methods. However, the highly localized and intense nature of these processes elicits many experimental and computational challenges. These challenges motivate a strong need for computational investigation, as does the need to more accurately characterize the response of parts built using AM. The present work will discuss these challenges and methods for creating multiscale material models that account for the complex phenomena observed in the AM production environment. The linkage between process, structure, and property [<span id='cite-2'></span>[[#2|2]]] of AM components, e.g., anisotropic plastic behavior [<span id='cite-3'></span>[[#3|3]]],[<span id='cite-4'></span>[[#4|4]]] combined anisotropic microstructural descriptors afforded through enhanced data compression techniques, will also be discussed.
  
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== Recording of the presentation ==
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|(<sup>1</sup>) Department of Mechanical Engineering
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| (<sup>2</sup>)Department of Materials Science and Engineering
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| Northwest University, Evanston, IL 60208-3111, USA
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|(<sup>3</sup>) Department of Mechanical Engineering
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| {{#evt:service=youtube|id=https://www.youtube.com/watch?v=t6tr73POWZ0|alignment=center}}
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|- style="text-align: center;"
| Tsinghua University, Beijing 100084, China
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| Location: Technical University of Catalonia (UPC), Vertex Building.
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|- style="text-align: center;"
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| Date: 1 - 3 September 2015, Barcelona, Spain.
 
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==Abstract==
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== General Information ==
Additive manufacturing (AM) processes have the ability to build complex geometries from a wide variety of materials. A popular approach for metal-based AM processes involves the deposition of material particles on a substrate followed by fusion of those particles together using a high intensity heat source, e.g. a laser or an electron beam, in order to fabricate a solid part. These methods are of high priority in engineering research,
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* Location: Technical University of Catalonia (UPC), Barcelona, Spain.
especially in applications for the energy, health, and defense sectors. The primary reasons behind the rapid growth in interest for AM include: (1) the ability to create complex geometries that are otherwise cost-prohibitive or difficult to manufacture, (2) increased freedom of material composition design through the adjustment of the elemental ratios of the composing powders, (3) a reduction in wasted materials, and (4) fast, low-volume, production of prototype and functional parts without the additional tooling and die requirements of conventional manufacturing methods. However, the highly localized and intense nature of these processes elicits many experimental and computational challenges. These challenges motivate a strong need for computational investigation, as does the need to more accurately characterize the response of parts built using AM. The present work will discuss these challenges and methods for creating multiscale material models that account for the complex phenomena observed in
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* Date: 1 - 3 September 2015
additively manufactured products. The linkage between process, structure, and property of AM components, e.g., anisotropic plastic behavior combined with anisotropic microstructural descriptors afforded through enhanced data compression techniques, will also be discussed.
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* Secretariat: [//www.cimne.com/ International Center for Numerical Methods in Engineering (CIMNE)].
 
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'''keywords''' Additive Manufacturing, Image-based Plasticity, Anisotropic Microstructure
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{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
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! Recording of the presentation
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|-
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| <embedvideo service="youtube">https://www.youtube.com/watch?v=t6tr73POWZ0</embedvideo>
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|}
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== External Links ==
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* [//congress.cimne.com/complas2015/frontal/default.asp Complas XIII] Official Website of the Conference.
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* [//www.cimnemultimediachannel.com/ CIMNE Multimedia Channel]
  
== Supplementary material ==
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==References==
* Description: [[File:Example.jpg]]
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<div id="1"></div>
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[[#cite-1|[1]]] Yan, W., Smith, J., Ge, W., Lin, F., Liu, W.K., “Multiscale modeling of electron beam and
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substrate interaction: a new heat source model,” Computational Mechanics, 1-12 (2015).
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<div id="2"></div>
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[[#cite-2|[2]]] O’Keeffe, C., Tang, S., Kopacz, A.M., Smith, J., Rowenhorst, D., Spanos, G., Liu, W.K., Olson,
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G.B., “Multiscale Ductile Fracture Integrating Tomographic Characterization and 3D Simulation,”
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Acta Materialia, 82, 503-510 (2015).
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<div id="3"></div>
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[[#cite-3|[3]]] Smith, J., Liu, W.K., Cao, J., “A General Anisotropic Yield Criterion for Pressure-Dependent
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Materials,” International Journal of Plasticity, accepted manuscript.
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<div id="4"></div>
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[[#cite-4|[4]]] Smith, J., Moore, J. A., Cao, J., Liu, W.K., “A General Anisotropic Yield Criterion for DamageProne
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Materials with Sensitivity to Shear Loading,” Journal of Mechanics and Physics of Solids, in
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preparation.

Latest revision as of 14:36, 19 July 2016

Abstract

Additive manufacturing (AM) processes have the ability to build complex geometries from a wide variety of materials. A popular approach for metal-based AM processes involves the deposition of material particles on a substrate followed by fusion of those particles together using a high intensity heat source, e.g. a laser or an electron beam [1], in order to fabricate a solid part. These methods are of high priority in engineering research, especially in applications for the energy, health, and defense sectors. The primary reasons behind the rapid growth in interest for AM include: (1) the ability to create complex geometries which are otherwise cost-prohibitive or difficult to manufacture, (2) increased freedom of material composition design through the adjustment of the ratios of the composing powders, (3) a reduction in wasted materials, and (4) the fast, low-volume, production of prototype and functional parts without the additional tooling and die requirements of conventional manufacturing methods. However, the highly localized and intense nature of these processes elicits many experimental and computational challenges. These challenges motivate a strong need for computational investigation, as does the need to more accurately characterize the response of parts built using AM. The present work will discuss these challenges and methods for creating multiscale material models that account for the complex phenomena observed in the AM production environment. The linkage between process, structure, and property [2] of AM components, e.g., anisotropic plastic behavior [3],[4] combined anisotropic microstructural descriptors afforded through enhanced data compression techniques, will also be discussed.

Recording of the presentation

Location: Technical University of Catalonia (UPC), Vertex Building.
Date: 1 - 3 September 2015, Barcelona, Spain.

General Information

External Links

References

[1] Yan, W., Smith, J., Ge, W., Lin, F., Liu, W.K., “Multiscale modeling of electron beam and substrate interaction: a new heat source model,” Computational Mechanics, 1-12 (2015).

[2] O’Keeffe, C., Tang, S., Kopacz, A.M., Smith, J., Rowenhorst, D., Spanos, G., Liu, W.K., Olson, G.B., “Multiscale Ductile Fracture Integrating Tomographic Characterization and 3D Simulation,” Acta Materialia, 82, 503-510 (2015).

[3] Smith, J., Liu, W.K., Cao, J., “A General Anisotropic Yield Criterion for Pressure-Dependent Materials,” International Journal of Plasticity, accepted manuscript.

[4] Smith, J., Moore, J. A., Cao, J., Liu, W.K., “A General Anisotropic Yield Criterion for DamageProne Materials with Sensitivity to Shear Loading,” Journal of Mechanics and Physics of Solids, in preparation.

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