Efficient Microstructure Characterization of Metals

Using Light Microscopy

21 April 2020 · 54 min watch
  • Software
  • Materials Sciences
Author Torben Wulff Solutions Manager, ZEISS Microscopy
Author Tim Schubert Materials Scientist at Materials Research Institute Aalen, Aalen University

Material’s properties are strongly linked to its microstructure, such as grain size, porosity, phase and non-metallic inclusions. Light microscopy is a powerful tool for evaluating a material’s microstructure. But extracting meaningful results using traditional image analysis can be challenging, especially for new materials or materials with multiple phases. For instance, magnetic materials being developed for use in electric motors consist of complex structures. Segmentation of these structures in different phases can prove difficult with traditional image analysis techniques.

Key Learnings:

  • How machine learning-based algorithms can be utilized to segment challenging images and help characterize functional materials and advanced metals
  • What is important when choosing a microscope for metallography
  • What the challenges are in characterizing new materials with multiple phases
  • How to overcome these challenges with machine learning tools

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