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Performance Assessment on Corrosion Resistance of Refractory Materials Based on High-temperature Machine Vision Technology

作者:Chenchen LIU1Ao HUANG1Yan YU2Guoping WEI2Shenghao LI1Huazhi GU1

作者單位:1.The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology2.Zhejiang Zili High Temperature Technology Co., Ltd.

刊名:China's Refractories

ISSN:1004-4493

出版年:2024-09-15

卷:33

期:3

起頁:42-48

止頁:

分類號:TP391.41;TQ175.7

語種:中文

關鍵詞:

內容簡介

Refractory materials,as the crucial foundational materials in high-temperature industrial processes such as metallurgy and construction,are inevitably subjected to corrosion and penetration from high-temperature media during their service.Traditionally,observing the in-situ degradation process of refractory materials in complex high-temperature environments has presented challenges.Post-corrosion analysis are commonly employed to assess the slag resistance of refractory materials and understand the corrosion mechanisms.However,these methods often lack information on the process under the conditions of thermal-chemical-mechanical coupling,leading to potential biases in the analysis results.In this work,we developed a non-contact high-temperature machine vision technology by the integrating Digital Image Correlation (DIC)with a high-temperature visualization system to explore the corrosion behavior of Al2O3-SiO2 refractories against molten glass and Al2O3-Mg O dry ramming refractories against molten slag at different temperatures.This technology enables realtime monitoring of the 2D or 3D overall strain and average strain curves of the refractory materials and provides continuous feedback on the progressive corrosion of the materials under the coupling conditions of thermal,chemical,and mechanical factors.Therefore,it is an innovative approach for evaluating the service behavior and performance of refractory materials,and is expected to promote the digitization and intelligence of the refractory industry,contributing to the optimization and upgrading of product performance.

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