In the field of materials science, accurate and precise material characterization is essential for understanding material properties. Manual microstructural classification is subjective, time-consuming, and prone to human error. There is a growing need for automation and reliable techniques to identify and classify microstructural features. This project aims to develop an integrated system utilizing computer vision, machine learning algorithms, and advanced deep learning techniques for automating and predicting material microstructure. Deep learning has already been applied for feature extraction and learning across various domains, including materials science. However, most applications focus either on complex industrial processes or data simulation, with few addressing property forecasting and analysis. Moreover, the limited programs available often require expensive equipment or high maintenance. The project is divided into three key steps: database creation, model training, and model evaluation. Database creation involves sample collection of grey cast iron, sample preparation (metallography), and microscopy to generate microstructural images. These images undergo feature engineering before being trained on different deep learning models. The model that performs best at 500 epochs with an image size of 1056 is selected, achieving a mean Average Precision (mAP) of 90.2% at a threshold of 0.5. The selected model is then evaluated on different grey cast iron microstructures to determine the percentage of various types of carbon flakes. This project introduces a Python-embedded machine learning program that automates carbon flake characterization using deep learning techniques with 90% accuracy. The proposed system contributes to the advancement of automated microstructural characterization of grey cast iron.
