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Journal of Materials Science

, Volume 48, Issue 12, pp 4191–4204 | Cite as

Prediction models for the yield strength of particle-reinforced unimodal pure magnesium (Mg) metal matrix nanocomposites (MMNCs)

  • Chang-Soo KimEmail author
  • Il Sohn
  • Marjan Nezafati
  • J. B. Ferguson
  • Benjamin F. Schultz
  • Zahra Bajestani-Gohari
  • Pradeep K. Rohatgi
  • Kyu Cho
Article

Abstract

Particle-reinforced metal matrix nanocomposites (MMNCs) have been lauded for their potentially superior mechanical properties such as modulus, yield strength, and ultimate tensile strength. Though these materials have been synthesized using several modern solid- or liquid-phase processes, the relationships between material types, contents, processing conditions, and the resultant mechanical properties are not well understood. In this paper, we examine the yield strength of particle-reinforced MMNCs by considering individual strengthening mechanism candidates and yield strength prediction models. We first introduce several strengthening mechanisms that can account for increase in the yield strength in MMNC materials, and address the features of currently available yield strength superposition methods. We then apply these prediction models to the existing dataset of magnesium MMNCs. Through a series of quantitative analyses, it is demonstrated that grain refinement plays a significant role in determining the overall yield strength of most of the MMNCs developed to date. Also, it is found that the incorporation of the coefficient of thermal expansion mismatch and modulus mismatch strengthening mechanisms will considerably overestimate the experimental yield strength. Finally, it is shown that work-hardening during post-processing of MMNCs employed by many researchers is in part responsible for improvement to the yield strength of these materials.

Keywords

Strengthening Mechanism Orowan Strengthen Orowan Mechanism Modulus Mismatch Geometrically Necessary Dislocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is primarily supported by the Research Growth Initiative (RGI) Award from University of Wisconsin-Milwaukee (UWM). Partial support from the U.S. Army Research Laboratory (US ARL) under Cooperative Agreement No. W911NF-08-2-0014 is also acknowledged. The views, opinions, and conclusions made in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Chang-Soo Kim
    • 1
    Email author
  • Il Sohn
    • 2
  • Marjan Nezafati
    • 1
  • J. B. Ferguson
    • 1
  • Benjamin F. Schultz
    • 1
  • Zahra Bajestani-Gohari
    • 1
  • Pradeep K. Rohatgi
    • 1
  • Kyu Cho
    • 3
  1. 1.Materials Science and Engineering DepartmentUniversity of Wisconsin-MilwaukeeMilwaukeeUSA
  2. 2.Materials Science and Engineering DepartmentYonsei UniversitySeoulSouth Korea
  3. 3.U.S. Army Research LaboratoryWeapons and Materials Research DirectorateAberdeen Proving GroundUSA

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