Research progress on characterization and evaluation of refractory damage behavior based on machine vision/hearing

LIU Chenchen, HUANG Ao, LI Shenghao, CHEN Xinyu, GU Huazhi

Journal of Iron and Steel Research ›› 2024, Vol. 36 ›› Issue (10) : 1247-1266.

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Journal of Iron and Steel Research ›› 2024, Vol. 36 ›› Issue (10) : 1247-1266. DOI: 10.13228/j.boyuan.issn1001-0963.20240167
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Research progress on characterization and evaluation of refractory damage behavior based on machine vision/hearing

  • LIU Chenchen, HUANG Ao, LI Shenghao, CHEN Xinyu, GU Huazhi
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Abstract

As an essential fundamental material in high-temperature industries such as metallurgy and building materials, refractories inevitably endure chemical corrosion and mechanical fracture (collectively referred to as "damage") under coupled thermal-chemical-mechanical conditions during the period of service, resulting in the deterioration and wear of high-temperature furnace linings and affecting quality of the products. Conducting postmortem testing analysis methods to evaluate damage behavior presents challenges, as direct observation of the in-situ degradation process of refractories within high-temperature and complex environments is not feasible. The paucity of process information, particularly regarding the condition of thermal-chemical-mechanical coupling, can potentially skew the analysis outcomes. The research progress in the application of machine vision/hearing technologies in characterizing and evaluating the damage behavior of refractories was reviewed. It indicates that the application of digital image correlation (DIC) and acoustic emission (AE) techniques, which achieve non-contact real-time monitoring of the full-field strain and acoustic emission signals of refractories, can characterize the degradation evolution process under multi-factor coupling conditions. These techniques provide a novel approach for accurately revealing the damage mechanisms and evaluating the service performance of refractories. This research aims to provide theoretical support for the development of superior refractories for high-quality product smelting and production.

Key words

refractory / digital image correlation (DIC) / acoustic emission (AE) / machine vision/hearing / damage behavior / characterization and evaluation

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LIU Chenchen, HUANG Ao, LI Shenghao, CHEN Xinyu, GU Huazhi. Research progress on characterization and evaluation of refractory damage behavior based on machine vision/hearing[J]. Journal of Iron and Steel Research, 2024, 36(10): 1247-1266 https://doi.org/10.13228/j.boyuan.issn1001-0963.20240167

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