Open AccessCalculation of phase fraction in steel microstructure images using random forest classifier
Author(s) -
Paul Angshuman,
Gangopadhyay Abhinandan,
Chintha Appa Rao,
Mukherjee Dipti Prasad,
Das Prasun,
Kundu Saurabh
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1154
Subject(s) - artificial intelligence , microstructure , random forest , computer science , classifier (uml) , pattern recognition (psychology) , discriminative model , entropy (arrow of time) , materials science , physics , metallurgy , quantum mechanics
Proportions of different phases (phase fraction) in the microstructures determine the quality of dual phase (DP) steel. So, calculation of phase fraction in the microstructures of steel samples is important for quality assurance. Manual calculation of phase fraction involves Le Pera etching of steel which is time consuming and dependent on operator efficiency. Calculation of phase fraction from Le Pera etched samples requires cumbersome manual observations. Nital etching is a faster alternative to Le Pera etching. However, due to lack of visually discriminative information, different phases cannot be identified manually from nital images. We propose a novel method for automatic calculation of phase fractions in steel microstructures from nital images using machine learning techniques. We show that regional contour patterns and local entropy (which cannot be evaluated manually) of regions of nital images are related to the formation process of the phases. We design a method that automatically evaluates regional contour patterns and local entropy from nital images of DP steel. Subsequently, we construct a random forest classifier that uses regional contour patterns and local entropy as features for classification of different phases. Our method is ∼150 times faster than manual classification. Experiments show close to 90% accuracy in classification.