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

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

80 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)521-541
Number of pages21
JournalComputational Mechanics
Volume61
Issue number5
DOIs
StatePublished - May 1 2018

Keywords

  • Additive manufacturing
  • Data mining
  • Material modeling
  • Thermal fluid flow

ASJC Scopus subject areas

  • Computational Mechanics
  • Ocean Engineering
  • Mechanical Engineering
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

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