Nanomechanical properties of thermal arc sprayed coating using continuous stiffness measurement and artificial neural network

Wai Yeong Huen*, Hyuk Lee, Vanissorn Vimonsatit, Priyan Mendis, Han Seung Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Instrumented indentation continuous stiffness measurement (CSM) method is applied to investigate the nanomechanical properties of the aluminum and zinc arc thermal spray aluminum coating. This study shows that individual component within a multi-phase material can be differentiated through the stiffness characteristic transition in a single indentation. Using this approach, the nanomechanical properties of the individual phases can be isolated and quantified using statistical deconvolution method. This paper further demonstrates that through the use of computational simulation and artificial neural network, the nanomechanical properties can be predicted based on experimental nanoindentation loading and unloading, where the load-unload responses of an individual material phase can be replicated once the nanomechanical properties are made known. This study shows that CSM method is able to predict the material's elasticity and plasticity properties, including elastic modulus, hardness, yield strength and work hardening, of individual aluminum and zinc components of the thermal arc spray coating.

Original languageEnglish
Pages (from-to)266-276
Number of pages11
JournalSurface and Coatings Technology
Volume366
DOIs
Publication statusPublished - 25 May 2019
Externally publishedYes

Keywords

  • Nanoindentation
  • Continuous stiffness measurement
  • Nanomechanical properties
  • Artificial neural network

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