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Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing

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Abstract

Additive manufacturing (AM) possesses appealing potential for manipulating material compositions, structures and properties in end-use products with arbitrary shapes without the need for specialized tooling. Since the physical process is difficult to experimentally measure, numerical modeling is a powerful tool to understand the underlying physical mechanisms. This paper presents our latest work in this regard based on comprehensive material modeling of process–structure–property relationships for AM materials. The numerous influencing factors that emerge from the AM process motivate the need for novel rapid design and optimization approaches. For this, we propose data-mining as an effective solution. Such methods—used in the process–structure, structure–properties and the design phase that connects them—would allow for a design loop for AM processing and materials. We hope this article will provide a road map to enable AM fundamental understanding for the monitoring and advanced diagnostics of AM processing.

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Acknowledgements

W. K. Liu, W. Yan and J. Cao acknowledge the support by National Institute of Standards and Technology (NIST) Center for Hierarchical Materials Design (CHiMaD) under grant No. 70NANB14H012. K. Ehmann, G. J. Wagner J. Cao and W. K. Liu acknowledge the support by the National Science Foundation (NSF) Cyber-Physical Systems (CPS) under grant No. CPS/CMMI-1646592. The support from DMDII (Digital Manufacturing Design Innovation Institute) is also acknowledged. S. Lin and O.L. Kafka acknowledge the support of the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1324585. The tomographic image of Figs. 16 and 17 used resources of the Advanced Photon Source (APS), a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. We thank Dr. Xianghui Xiao and Dr. Tao Sun for the assistance at APS beamlines.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60201, USA

    Wentao Yan, Stephen Lin, Orion L. Kafka, Yanping Lian, Cheng Yu, Zeliang Liu, Jinhui Yan, Sarah Wolff, Hao Wu, Ebot Ndip-Agbor, Mojtaba Mozaffar, Kornel Ehmann, Jian Cao, Gregory J. Wagner & Wing Kam Liu

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  1. Wentao Yan
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Correspondence to Wing Kam Liu.

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Yan, W., Lin, S., Kafka, O.L. et al. Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing. Comput Mech 61, 521–541 (2018). https://doi.org/10.1007/s00466-018-1539-z

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