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About this Abstract
Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Microstructure-Sensitive Segmentation of γ′ Phase in Superalloys Using Detectron2 for Data-Driven Alloy Design and Process Optimisation
Author(s) Isaac Ifeanyi Iwediba, Vassili Vorontsov
On-Site Speaker (Planned) Isaac Ifeanyi Iwediba
Abstract Scope A dataset of 99 high-resolution microstructure images was used to train and validate (in a 80:20 ratio) a deep learning model for the segmentation of γ′ (gamma-prime) precipitates in electron micrographs of high-temperature superalloys. The dataset was prepared by manual annotation images in CVAT. From the initial dataset, >13,000 samples were generated through additional augmentation. The Detectron2 library, with a Mask R-CNN model, was then used for pixel-wise segmentation. The trained model achieved high segmentation accuracy and repeatability on the test dataset, performing well across a variety of γ′ precipitate morphologies. The precision and throughput of the technique were higher than traditional image processing and other deep learning-based methods. Wider application of the model to larger-scale analyses is also examined, using ROI particle size distributions and area fraction measurements from automated image segmentation to provide quantitative data that will influence alloy design and manufacturing process decisions in industry.
Proceedings Inclusion? Undecided

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