HawkEye: AI-Driven Software for Objective Analysis and Characterization of Nodular Cast Iron Microstructures

Metallographic evaluation of nodular cast iron is crucial for quality control in the foundry industry. Traditionally, this process relies on experts who visually interpret microscopic images. This study introduces HawkEye, a comprehensive software solution that automates metallographic analysis using advanced computer vision and deep learning models. Specifically, HawkEye software dynamically adapts its processing workflow based on the input image and its typological classification. The software supports both etched and non-etched specimens and automates the segmentation and classification of graphite nodules, gathering their morphological descriptors; it identifies microstructural phases and provides a global quality assessment. All these functions are embedded into a user-friendly interface designed for both laboratory and industrial use. Nevertheless, the key contribution of this work is the replacement of subjective evaluation with a reproducible, AI-driven approach, which significantly enhances the objectivity, traceability, and scalability of metallurgical analysis. In fact, the proposed approach achieves 99% accuracy in nodule classification compared to manual expert assessment, reduces manual image processing steps, and introduces a novel method for ferrite/perlite measurement in combination with carbide detection using YOLO and SAM models.