Szczegóły publikacji
Opis bibliograficzny
Supervised pearlitic–ferritic steel microstructure segmentation by U‑Net convolutional neural network / Mateusz MOTYL, Łukasz MADEJ // Archives of Civil and Mechanical Engineering / Polish Academy of Sciences. Wrocław Branch, Wrocław University of Technology ; ISSN 1644-9665. — 2022 — vol. 22 iss. 4 art. no. 206, s. 1–13. — Bibliogr. s. 12–13, Abstr. — Publikacja dostępna online od: 2022-09-21
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 143262 |
|---|---|
| Data dodania do BaDAP | 2022-10-25 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s43452-022-00531-4 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Archives of Civil and Mechanical Engineering |
Abstract
The aim of this work is to develop an automated procedure based on machine learning capabilities for the identification of the pearlite islands within the two-phase pearlitic–ferritic steel. The input parameters for the custom implementation of a braided neural network are provided as a data set of scanning electron microscopy images of metallographic specimens. The procedures related to the processing of the data and the optimization parameters affecting the final architecture and effectiveness of the network learning stage are examined. The objective is to find the best solution to the problem of ferritic–pearlitic microstructure segmentation, allowing further processing during, e.g., 3D reconstruction of data from serial sectioning. The work examines the various quality of input data and different U-Net architectures to find the one that can identify pearlite islands with the highest precision. Two types of images acquired from secondary electron (SE) and electron backscattered diffraction (EBSD) detectors are used during the investigation. The work revealed that the developed approach offers improvements in metallographic investigations by removing the requirement for expert knowledge for the interpretation of image data prior to further characterization. It has also been proven that artificial neural networks based on the deep learning process using extensible U-Net network architectures and nonlinear learning tools can identify pearlite islands within a two-phase microstructure, while the overtraining level remains low. Convolutional neural networks do not require manual feature extraction and are able to automatically find appropriate search functions to recognize pearlite structure areas in the training process without human intervention. It was shown that the network recognizes areas of analyzed steel with satisfactory precision of 79% for EBSD and 87% for SE images.