Development of Auto-Searching Method of Brittle Fracture Initiation                        Point Based on River-Pattern and Tear Ridge

Finding the fracture initiation sites is critical for understanding the fracture mechanism and thus improving the products' quality, whereas some hard-to-detected features are easily missed in the human-effortbased characterizations with human eye and involve lots of labor force. In this study, computer-aided detection of fracture initiation sites is proposed to augment human expertise to efficiently find the fracture initiation sites, and thus to reduce the labor cost. With a deep-learning you only look once object detector, the fracture initiation sites of steel were successfully detected in this study. Furthermore, based on the trained detection model, an easy-to-use application for detecting initiation sites has been further developed, exhibiting great potential for high-efficiency detection of fracture initiation sites.

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Computer-aided High-efficiency Detection of Fracture Initiation Sites in Steel

Muramatsu

Wang

Ogawa

et al. 2022

ISIJ Int.

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Machine Learning-Aided Analysis of the Rolling and Recrystallization Textures of Pure Iron with Different Cold Reduction Ratios and Cold-Rolling Directions

Sumida,

Sugiura,

Ogawa

et al. 2024

Materials

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We performed a machine learning-aided analysis of the rolling and recrystallization textures in pure iron with different cold reduction ratios and cold-rolling directions. Five types of specimens with different cold reduction ratios and cold-rolling directions were prepared. The effect of two-way cold-rolling on the rolling texture was small at cold reduction ratios different from 60%. The cold reduction ratio in each stage hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. In the case of long-term annealing, although abnormal grain growth occurred, the crystal orientation of the grains varied. Moreover, the direction of cold-rolling in each stage also hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. During long-term annealing, sheets with the same cold-rolling direction in the as-received state and in the first stage showed the texture evolution of conventional one-way cold-rolled pure iron. Additionally, we conducted a machine learning-aided analysis of rolling and recrystallization textures. Using cold-rolling and annealing conditions as the input data and the degree of Goss orientation development as the output data, we constructed high-accuracy regression models using artificial neural networks and XGBoost. We also revealed that the annealing temperature is the dominant factor in the nucleation of Goss grains.

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Development of Auto-Searching Method of Brittle Fracture Initiation Point Based on River-Pattern and Tear Ridge

Cited by 2 publications

References 20 publications

Computer-aided High-efficiency Detection of Fracture Initiation Sites in Steel

Computer-aided High-efficiency Detection of Fracture Initiation Sites in Steel

Machine Learning-Aided Analysis of the Rolling and Recrystallization Textures of Pure Iron with Different Cold Reduction Ratios and Cold-Rolling Directions

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