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Automatic recognition of dendritic solidification structures: DenMap

  • Bogdan Nenchev
  • , Joel Strickland
  • , Karl Tassenberg
  • , Samuel Perry
  • , Simon Gill
  • , Hongbiao Dong*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Dendrites are the predominant solidification structures in directionally solidified alloys and control the maximum length scale for segregation. The conventional industrial method for identification of dendrite cores and primary dendrite spacing is performed by time-consuming laborious manual measurement. In this work we developed a novel DenMap image processing and pattern recognition algorithm to identify dendritic cores. Systematic row scan with a specially selected template image over an image of interest is applied via a normalised cross-correlation algorithm. The DenMap algorithm locates the exact dendritic core position with a 98% accuracy for a batch of SEM images of typical as-cast CMSX-4® microstructures in under 90 s per image. Such accuracy is achieved due to a sequence of specially selected image pre-processing methods. Coupled with statistical analysis the model has the potential to gather large quantities of structural data accurately and rapidly, allowing for optimisation and quality control of industrial processes to improve mechanical and creep performance of materials.

Original languageEnglish
Article number6040019
JournalJournal of Imaging
Volume6
Issue number4
DOIs
Publication statusPublished - 3 Apr 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors.

Keywords

  • 2-D quantitative analysis
  • Directional solidification
  • Image analysis
  • Pattern recognition
  • Superalloy

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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