Classification of non-metallic inclusions

Task definition:

  • To determine the chemical composition of submicroscopic, non-metallic inclusions (NMI) in steel, analysis using a scanning electron microscope (SEM) with energy-dispersive X-ray spectroscopy (EDX) is widely used (resolution limit approx. 200-300 nm)

  • However, since information about the chemical composition can also be derived from the backscattered electron (BSE) detector image of the SEM or the particle shape, it was investigated to what extent machine learning (ML) can be used for purely image-based classification without relying on complex EDX measurements


  • Data set: BSE images of the NMI, with associated ground truth is determined from the EDX spectrum
  • Use of Deep Learning (DL): classification model based only on the BSE images
  • In this application example, 5 classes are considered: Carbonitrides, oxides, sulphides, mixed oxide-sulphides as well as “non-inclusions” (e.g. pores, scratches, contaminations)

BSE recordings of the 5 classes


  • The DL model achieved an 84.9% accuracy in determining the chemical composition of the NMI using only the BSE image

  • Specifically, carbonitrides, sulphides and “non inclusions” can reliably be classified

Fields of application:

  • Time saving by substituting EDX measurement with pure image evaluation

Partners for cooperation:

  • Joint-stock company of Dillinger Huettenwerke, Dillingen/Saar
  • Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh


Dr.-Ing. Dominik Britz

Deputy Head MECS Saarbrücken

+49 681 302 70540

Adrian Thome, M.Sc.

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