PSI - Issue 74

Tomáš Vražina et al. / Procedia Structural Integrity 74 (2025) 106 –113 Tomáš Vražina / Structural Integrity Procedia 00 (202 5 ) 000 – 000

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Liu, L., Ding, Q., Zhong, Y., Zou, J., Wu, J., Chiu, Y.-L., Li, J., Zhang, Z., Yu, Q., Shen, Z., 2018. Dislocation network in additive manufactured steel breaks strength–ductility trade-off. Materials Today 21, 354–361. Mikmeková, Š., Man, J., Ambrož, O., Jozefovič, P., Čermák, J., Järvenpää, A., Jaskari, M., Materna, J., Kruml, T., 2023. High -Resolution Characterization of Deformation Induced Martensite in Large Areas of Fatigued Austenitic Stainless Steel Using Deep Learning. Metals 13, 1039. Na, J., Kim, S.-J., Kim, H., Kang, S.-H., Lee, S., 2023. A unified microstructure segmentation approach via human-in-the-loop machine learning. Acta Materialia 255, 119086. Riabov, D., Leicht, A., Ahlström, J., Hryha, E., 2021. Investigation of the strengthening mechanism in 316L stainless steel produced with laser powder bed fusion. Materials Science and Engineering: A 822, 141699. Ronneberger, O., Fischer, P., Brox, T., 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing, Cham, pp. 234–241. Saeidi, K., Gao, X., Zhong, Y., Shen, Z.J., 2015. Hardened austenite steel with columnar sub-grain structure formed by laser melting. Materials Science and Engineering: A 625, 221–229. Sev ı̇, M., Aydin, İl. , 2023. Improving Unet segmentation performance using an ensemble model in images containing railway lines. Turkish Journal of Electrical Engineering and Computer Sciences 31, 739–750. Shih, C.-C., Shih, C.-M., Su, Y.-Y., Su, L.H.J., Chang, M.-S., Lin, S.-J., 2004. Effect of surface oxide properties on corrosion resistance of 316L stainless steel for biomedical applications. Corrosion Science 46, 427–441. Stuckner, J., Harder, B., Smith, T.M., 2022. Microstructure segmentation with deep learning encoders pre-trained on a large microscopy dataset. npj Comput Mater 8, 1–12. Wang, X., Nadimpalli, V.K., Tiedje, N.S., Juul Jensen, D., Yu, T., 2025. Additive-Manufacturing-Induced Cell Structure in Stainless Steel 316L: 3D Morphology and Formation Mechanism. Metall Mater Trans A 56, 506–517. Zhang, T., Ramakrishnan, R., Livny, M., 1996. BIRCH: an efficient data clustering method for very large databases. SIGMOD Rec. 25, 103–114. Zhou, P., Zhang, X., Shen, X., Shi, H., He, J., Zhu, Y., Jiang, F., Yi, F., 2024. Multi-phase material microscopic image segmentation for microstructure analysis of superalloys via modified U-Net and rectify strategies. Computational Materials Science 242, 113063.

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