{"title":"Extracting ductile cast iron microstructure parameters from fracture surfaces: A deep learning based instance segmentation approach","authors":"","doi":"10.1016/j.engfracmech.2024.110586","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigates the deep-learning based microstructural analysis from SEM images of ductile cast iron fracture surfaces. A Mask R-CNN model was trained, achieving 70% precision and 75% recall in graphite particle detection. Combined with a fracture surface reconstruction using the.</div><div>4-quadrant backscattered electron signal, key parameters, including the particle size, shape and distance were extracted accurately. Compared to micrograph analysis, following probabilistic simulations showed the impact of the higher microstructural variance for the fracture surfaces on crack initiation, leading to higher scatter and elevated crack resistance curves. This highlights the potential of deep-learning based analysis for comprehensive microstructural characterization.</div></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Fracture Mechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0013794424007495","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MECHANICS","Score":null,"Total":0}
引用次数: 0
Abstract
This study investigates the deep-learning based microstructural analysis from SEM images of ductile cast iron fracture surfaces. A Mask R-CNN model was trained, achieving 70% precision and 75% recall in graphite particle detection. Combined with a fracture surface reconstruction using the.
4-quadrant backscattered electron signal, key parameters, including the particle size, shape and distance were extracted accurately. Compared to micrograph analysis, following probabilistic simulations showed the impact of the higher microstructural variance for the fracture surfaces on crack initiation, leading to higher scatter and elevated crack resistance curves. This highlights the potential of deep-learning based analysis for comprehensive microstructural characterization.
期刊介绍:
EFM covers a broad range of topics in fracture mechanics to be of interest and use to both researchers and practitioners. Contributions are welcome which address the fracture behavior of conventional engineering material systems as well as newly emerging material systems. Contributions on developments in the areas of mechanics and materials science strongly related to fracture mechanics are also welcome. Papers on fatigue are welcome if they treat the fatigue process using the methods of fracture mechanics.