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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Nanotechnology

Background:

  • Nanoindentation is crucial for nanoscale mechanical property evaluation.
  • Conventional methods struggle with targeted analysis of microstructural features.
  • A need exists for automated, precise nanoindentation workflows.

Purpose of the Study:

  • To develop an automated nanoindentation framework supporting machine learning.
  • To enhance precision and efficiency in characterizing complex materials.
  • To enable feature-based and large-scale automated indentation.

Main Methods:

  • Implemented a three-mode automated system: standard, feature-based (image-to-coordinate mapping), and large-scale.
  • Utilized direct sample-to-indenter alignment to minimize travel errors.
  • Employed pixel-to-micron calibration for accurate navigation and Self-Organizing Feature Maps for guided indentation.

Main Results:

  • Achieved precise, automated indentation with reduced user intervention.
  • Demonstrated phase-specific and orientation-guided indentation capabilities.
  • Mitigated initial travel-distance errors by 2.5-6 μm through direct alignment.

Conclusions:

  • The developed framework significantly enhances precision and efficiency in nanoindentation.
  • It enables targeted characterization of microstructurally complex materials.
  • The adaptable interface supports integration with existing nanoindenter platforms for autonomous testing.