Cementron: Machine Learning the Constituent Phases in Cement Clinker from Optical Images

TL;DR

This paper introduces Cementron, a machine learning model trained on annotated optical images to automatically identify and segment key phases in cement clinker, improving analysis accuracy and consistency over traditional methods.

Contribution

The paper presents the first annotated dataset of cement clinker phases and develops a novel ML model, Cementron, for automatic phase detection using supervised learning.

Findings

Cementron accurately detects clinker phases in new images.

The model generalizes well across different imaging conditions.

Publicly available for community use.

Abstract

Cement is the most used construction material. The performance of cement hydrate depends on the constituent phases, viz. alite, belite, aluminate, and ferrites present in the cement clinker, both qualitatively and quantitatively. Traditionally, clinker phases are analyzed from optical images relying on a domain expert and simple image processing techniques. However, the non-uniformity of the images, variations in the geometry and size of the phases, and variabilities in the experimental approaches and imaging methods make it challenging to obtain the phases. Here, we present a machine learning (ML) approach to detect clinker microstructure phases automatically. To this extent, we create the first annotated dataset of cement clinker by segmenting alite and belite particles. Further, we use supervised ML methods to train models for identifying alite and belite regions. Specifically, we finetune the image detection and segmentation model Detectron-2 on the cement microstructure to develop a model for detecting the cement phases, namely, Cementron. We demonstrate that Cementron, trained only on literature data, works remarkably well on new images obtained from our experiments, demonstrating its generalizability. We make Cementron available for public use.