Automatic Recognition of Dendritic Solidification Structures: DenMap.

Bogdan Nenchev,Simon Gill,Samuel Perry,Hongbiao Dong,Joel Strickland,Karl Tassenberg

doi:10.3390/jimaging6040019

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.

Highlights

The dendritic structure is one of the most complex forms of crystallisation in nature and technology
Primary dendrite arm spacing (PDAS) controls the maximum length scale for segregation [1,2,3], which determines the propensity for defect formation [3,4], the solutioning heat treatment time [5], and mechanical properties [6]
This results in the formation of low melting point secondary phase eutectics, as well as incoherent precipitates and pores in the interdendritic region

Summary

Introduction

The dendritic structure is one of the most complex forms of crystallisation in nature and technology. The accuracy of such an algorithm is poor because intersections at the dendrite core can algorithm relies on identifying binary crosses and performing statistical comparison between lengths diverge massively from an ideal binary cross and a large variety of branch lengths and random features of branches.

Micrograph

Normalised

Filtering—Improving Accuracy

Findings

Conclusions