J. Mater. Sci. Technol. ›› 2019, Vol. 35 ›› Issue (6): 1137-1146.DOI: 10.1016/j.jmst.2018.12.011

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Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens

H.Y. Wanab, G.F. Chenc, C.P. Lic, X.B. Qicd, G.P. Zhanga*()   

  1. a Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, 72 Wenhua Road, Shenyang, 110016, China
    b School of Materials Science and Engineering, University of Science and Technology of China, Shenyang, 110016, China
    c Materials & Manufacturing Qualification Group, Corporate Technology, Siemens Ltd., China, Beijing, 100102, China
    dState Key Laboratory of Tribology, Tsinghua University, Beijing, 100084, China
  • Received:2018-11-01 Revised:2018-11-26 Accepted:2018-11-28 Online:2019-06-20 Published:2019-06-19
  • Contact: Zhang G.P.
  • About author:

    1 These authors contributed equally to this work.

Abstract:

This overview firstly introduces the state-of-the-art research progress in length scale-related fatigue performance of conventionally-fabricated metals evaluated by miniature specimens. Some key factors for size effects sensitive to microstructures including the specimen thickness, grain size and a ratio between them are highlighted to summarize some general rules for size effects. Then, ongoing research progress and new challenges in evaluating the fatigue performance of additive manufactured parts controlled by location-specific defects, microstructure heterogeneities as well as mechanical anisotropy using miniature specimen testing technique are discussed and addressed. Finally, a potential roadmap to establish a data-driven evaluation platform based on a large number of miniature specimen-based experiment data, theoretical computations and the ‘big data’ analysis with machine learning is proposed. It is expected that this overview would provide a novel strategy for the realistic evaluation and fast qualification of fatigue properties of additive manufactured parts we have been facing to.

Key words: Additive manufacturing, Miniature specimen, Fatigue, Size effect, Location-specific, Data analysis