Cementron: Machine learning the alite and belite phases in cement clinker from optical images

Abstract

The performance of cement hydrate depends on constituent phases, viz., alite, belite, aluminate, and ferrites present in cement clinker. Traditionally, clinker phases are analyzed from the optical image using empirical image processing techniques. However, the non-uniformity of images, variations in geometry, size of phases, experimental approaches, and imaging methods make it challenging to identify individual phases. Here, we present a machine learning (ML) approach to automatically and accurately detect the major clinker microstructure phases, namely, alite and belite, from optical microscopy images. To this extent, we create an annotated dataset of cement clinker by manually labelling alite and belite phases in microscopy images. We then demonstrate the use of supervised ML methods to train models for identifying alite and belite regions by using a small fraction of annotations on a single image. Further, we finetune the image detection and segmentation Mask R-CNN model using Detectron-2 by considering all the annotated images of the cement microstructure to develop a model for detecting cement phases, namely, Cementron. We demonstrate that Cementron, trained on literature data, generalized well on completely new data obtained from in-house experiments, demonstrating its wider applicability.