Application of Deep learning to the segmentation of synchrotron X‑ray tomography data of multiphase metal matrix composites — ICTMS Committee Dep of Applied Maths Australian National University
  1. Schedule
  2. Theme 3: Advanced-materials e.g. composites & Foams
  3. Application of Deep learning to the segmentation of synchrotron X‑ray tomography data of multiphase metal matrix composites

Application of Deep learning to the segmentation of synchrotron X‑ray tomography data of multiphase metal matrix composites (70)

Sergei Evsevleev 1 , Sidnei Paciornik 1 2 , Christian Gollwitzer 1 , Giovanni Bruno 1
  1. Bundesanstalt für Materialforschung und –prüfung (BAM), Berlin, BERLIN, Germany
  2. DEQM PUC-Rio, Rio de Janeiro, Brazil

The 3D microstructure of an Al alloy matrix composite with two ceramic reinforcements was investigated bysynchrotron X-ray tomography. Adeep learningalgorithm was usedforthesegmentation of four differentphases. We show that convolutional networkswith the U-Net architecture areable tosolve complex segmentation tasks with small amount of training data.

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