Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

Zeyu Xia, Kan Ma, Sibo Cheng*, Thomas Blackburn, Ziling Peng, Kewei Zhu, Weihang Zhang, Dunhui Xiao, Alexander Knowles, Rossella Arcucci

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

The performance of advanced materials for extreme environments is underpinned by their microstructure, such as the size and distribution of nano- to micro-sized reinforcing phase(s). Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, e.g., Concentrated Solar Power. Their development requires the determination of precipitate volume fraction and size distribution using EM, as these properties are crucial for the thermal stability and mechanical properties of chromium superalloys. Traditional approaches to EM image processing utilise filtering with a fixed contrast threshold, leading to weak robustness to background noise and poor generalisability to different materials. It also requires an enormous amount of time for manual object measurements. Efficient and accurate object detection and segmentation are therefore highly desired to accelerate the development of novel materials like chromium-based superalloys. To address these bottlenecks, based on YOLOv5 and SegFormer structures, this study proposes an end-to-end, two-stage deep learning scheme, DT-SegNet, to perform object detection and segmentation for EM images. The proposed approach can thus benefit from the training efficiency of CNNs at the detection stage (i.e., a small number of training images required) and the accuracy of the ViT at the segmentation stage. Extensive numerical experiments demonstrate that the proposed DT-SegNet significantly outperforms the state-of-the-art segmentation tools offered by Weka and ilastik regarding a large number of metrics, including accuracy, precision, recall and F1-score. This model will be a meaningful tool for accelerating alloy development and microstructure examination.
Original languageEnglish
JournalPhysical Chemistry Chemical Physics
Early online date26 May 2023
DOIs
Publication statusE-pub ahead of print - 26 May 2023

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