Calculation of phase fraction in steel microstructure images using random forest classifier. Issue 8 (1st August 2018)

• Record Type: Journal Article
• Authors: Paul, Angshuman; Gangopadhyay, Abhinandan; Chintha, Appa Rao; Mukherjee, Dipti Prasad; Das, Prasun; Kundu, Saurabh
• Is Part Of: IET image processing. Volume 12:Issue 8(2018)
• Page Start: 1370
• Page End: 1377
• Publication Date: 2018-08-01
• Subjects: feature extraction -- image classification -- learning (artificial intelligence) -- steel -- etching; phase fraction -- Le Pera etching -- nital images -- steel microstructure images -- dual phase steel -- random forest classifier; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.1154
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.