Modeling process-structure-property relationships for additive manufacturing

Wentao Yan, Stephen Lin, Orion L. Kafka, Cheng Yu, Zeliang Liu, Yanping Lian, Sarah Wolff, Jian Cao, Gregory J. Wagner, Wing Kam Liu*

*Corresponding author for this work

Research output: Contribution to journalReview article

16 Scopus citations

Abstract

This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing (AM) materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the process-structure relationship, the multi-scale multi-physics process modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a high-efficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.

Original languageEnglish (US)
Pages (from-to)482-492
Number of pages11
JournalFrontiers of Mechanical Engineering
Volume13
Issue number4
DOIs
StatePublished - Dec 1 2018

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Keywords

  • additive manufacturing
  • data mining
  • material modeling
  • thermal fluid flow

ASJC Scopus subject areas

  • Mechanical Engineering

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