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RESEARCH PAPER · Materialia · 2023

Machine learning-based quantitative analysis of metal ductile fracture surface

Neng Zhang, Hao Fu, Pei Liu, Cynthia Wang, Yan Zhang, Turab Lookman, Haiyou Huang, Yanjing Su

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Machine learning-based quantitative analysis of metal ductile fracture surfaceOriginalEffect of multi-stage heat treatment on the microstructure and mechanical properties of Ti–6Al–4V alloy deposited by high-power laser melting deposition25Data-driven analysis of spinodoid topologies: anisotropy, inverse design, and elasticity tensor distribution9A new machine learning-based evaluation of ductile fracture18Fusing image and physical data for fatigue life prediction of nickel-based single crystal superalloys9Toughness from Imagery: Extracting More from Failure Analysis using Deep Convolutional Neural Networks2Curvature-driven quantification of 2D SEM fracture morphology across metallic materials0Application of AI/ML Based Image Analytics in Auto Component Fracture Analysis0Image-Based Machine Learning Methods in Materials Microstructure and Failure Analysis1Phase-field-informed machine learning on creep behavior of Ni-based single-crystal superalloys5Metamodeling of Void Closure during Hot Flat Rolling0Hydrogen-assisted cracking: A deep learning approach for fractographic analysis7Application of an Improved Dual-Branch Model Based on Multi-Scale Feature Fusion in Fracture Surface Image Recognition1Rapid Discovery of High-Performance Additive Manufacturing Superalloys Using High-Throughput and Artificial Intelligence Methods0Energy-fractal characterization and correlation of fracture behavior in pipeline girth welds0Curvature-driven quantification of 2D SEM fracture morphology across metallic materials0Curvature-driven quantification of 2D SEM fracture morphology across metallic materials1

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16 direct citing papers saved.

  1. Effect of multi-stage heat treatment on the microstructure and mechanical properties of Ti–6Al–4V alloy deposited by high-power laser melting deposition

    Materials Science and Engineering A · 2024-02-08

    Shimei Tao, Zhi Ting Geng, Zhenlin Zhang, Yan Liu, Haizhou Li, Xie Shao, Pu Xie, Hui Chen

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  2. Data-driven analysis of spinodoid topologies: anisotropy, inverse design, and elasticity tensor distribution

    International Journal of Mechanics and Materials in Design · 2024-03-15

    Farshid Golnary, Mohsen Asghari

    Read original paper ↗ ·

  3. A new machine learning-based evaluation of ductile fracture

    Engineering Fracture Mechanics · 2024-04-12

    Carlos Avilés‐Cruz, Miriam Aguilar-Sanchez, Benjamin Vargas-Arista, Elizabeth Garfias-García

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  4. Fusing image and physical data for fatigue life prediction of nickel-based single crystal superalloys

    Engineering Failure Analysis · 2024-04-24

    Zhuohan Li, Tianli Zhao, Jing Zhang, Junlin Hu, Yiliang You

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  5. Toughness from Imagery: Extracting More from Failure Analysis using Deep Convolutional Neural Networks

    Journal of Failure Analysis and Prevention · 2024-11-16

    Nathan Bianco, Kaitlynn Fitzgerald, Dale Cillessen, Nathan Brown, Jay D. Carroll, Anthony P. Garland, Kimberly L. Bassett, Jacob B. Schroder, Brad L. Boyce

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  6. Curvature-driven quantification of 2D SEM fracture morphology across metallic materials

    SSRN Electronic Journal · 2025-01-01

    Jingsi Jiao

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  7. Application of AI/ML Based Image Analytics in Auto Component Fracture Analysis

    SAE technical papers on CD-ROM/SAE technical paper series · 2025-03-31

    Priyabrata Sahoo, Sudhanshu Rawat, Vipin Garg, Garima Naidu, Amit Kumar Sharma, Rahul Narula, Ritesh Bindra, Pankaj Khera, Pooja Goel, Arup Mondal

    Read original paper ↗ ·

  8. Image-Based Machine Learning Methods in Materials Microstructure and Failure Analysis

    SAE technical papers on CD-ROM/SAE technical paper series · 2025-03-31

    Meysam Akbari, Andy Yu-Der Wang, Q. G. Wang, Cuifen Yan

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  9. Phase-field-informed machine learning on creep behavior of Ni-based single-crystal superalloys

    Journal of Materials Informatics · 2025-04-21

    Jiao Song, Shan Ye, Zan Zhang, Shenglong Wang, Haiwei Zhang, Muhammad Suleman, Haiyou Huang, Yongsheng Li

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  10. Metamodeling of Void Closure during Hot Flat Rolling

    steel research international · 2025-06-27

    Jannik Gerlach, Conrad Liebsch, Emad Scharifi

    Read original paper ↗ ·

  11. Hydrogen-assisted cracking: A deep learning approach for fractographic analysis

    Computational Materials Science · 2025-11-07

    Alessandro Campari, Florian Konert, Nima Razavi, Oded Sobol, Antonio Alvaro

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  12. Application of an Improved Dual-Branch Model Based on Multi-Scale Feature Fusion in Fracture Surface Image Recognition

    Materials · 2025-11-19

    Fei Gao, Denghui Wang, Fulai Yang, Mingping Zhou, Yuan Li, Zhen Zheng, Jianpeng Shi, Zheng Zhang

    Read original paper ↗ ·

  13. Rapid Discovery of High-Performance Additive Manufacturing Superalloys Using High-Throughput and Artificial Intelligence Methods

    Metallurgical and Materials Transactions A · 2025-12-16

    Pei Liu, Xueting Ren, Shuai Guan, Guangbao Sun, Guohao Liu, Xin Luo, Xiao Sun, Teng An, Zhongnan Bi

    Read original paper ↗ ·

  14. Energy-fractal characterization and correlation of fracture behavior in pipeline girth welds

    Engineering Failure Analysis · 2025-12-19

    Xingfeng Liu, Yongxin Xu, Ying Zhen, Jianmin Chen, Guiyi Wu, Yuguang Cao

    Read original paper ↗ ·

  15. Curvature-driven quantification of 2D SEM fracture morphology across metallic materials

    SSRN Electronic Journal · 2026-01-01

    Jingsi Jiao

    Read original paper ↗ ·

  16. Curvature-driven quantification of 2D SEM fracture morphology across metallic materials

    Materials Characterization · 2026-03-27

    Jingsi Jiao

    Read original paper ↗ ·
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