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Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support

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Additive manufacturing (AM) methods for rapid prototyping of 3D materials (3D printing) have become increasingly popular with a particular recent emphasis on those methods used for metallic materials. These processes typically involve an accumulation of cyclic phase changes. The widespread interest in these methods is largely stimulated by their unique ability to create components of considerable complexity. However, modeling such processes is exceedingly difficult due to the highly localized and drastic material evolution that often occurs over the course of the manufacture time of each component. Final product characterization and validation are currently driven primarily by experimental means as a result of the lack of robust modeling procedures. In the present work, the authors discuss primary detrimental hurdles that have plagued effective modeling of AM methods for metallic materials while also providing logical speculation into preferable research directions for overcoming these hurdles. The primary focus of this work encompasses the specific areas of high-performance computing, multiscale modeling, materials characterization, process modeling, experimentation, and validation for final product performance of additively manufactured metallic components.

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The authors would like to gratefully acknowledge the support for this work provided by National Institute of Standards and Technology (NIST) and Center for Hierarchical Materials Design (CHiMaD) under grant No. 70NANB13Hl94 and 70NANB14H012. Jacob Smith would like to acknowledge the United States Department of Defense for their support through the National Defense Science and Engineering Graduate (NDSEG) fellowship award. Orion L. Kafka would like to thank the United States National Science Foundation (NSF) for their support through the NSF Graduate Research Fellowship Program (GRFP) under financial award number DGE-1324585. The authors would also like to acknowledge Sarah Wolff and Fuyao Yan, both at Northwestern University, for their intellectual contributions through discussion on experimental characterization of mechanical behavior and microstructure resulting from AM processes.

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Smith, J., Xiong, W., Yan, W. et al. Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support. Comput Mech 57, 583–610 (2016).

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