作者：HIROSHI KUBOTA;YUSUKE URITA;MITSUO SUGAWARA;
作者單位：
刊名：Taikabutsu Overseas
ISSN：0285-0028
出版年：2001-01-05
卷：21
期：1
起頁：11
止頁：19
分類號(hào)：TQ175
語種：英文
關(guān)鍵詞：
內(nèi)容簡介Alumina-magnesia monolithic refractory materials are the prevailing materials for application in steel ladle linings because of their excellent service life which is due to spinel formation under elevated operating temperatures as well as to the ensuing densified structure.~(1)2)3)) However, to further enhance the durability of the materials their thermal spalling resistance must be improved, because they occasionally show considerable peeling, caused by cracks in the early stages of their service life. Effective measures, based on analysis of their peeling behaviour, will be required for further improvement. The thermal spalling behaviour has been the subject of various studies but the analysis has not yet been perfected to obtain sufficiently effective measures.~(4)5))For a quantitative evaluation of the spalling process, the thermal stress which develops in the ladle lining during service must be accurately determined through structural analysis; several properties of the materials, like expansion / contraction, elasto-plastic behaviour and its temperature dependence, creep and its temperature dependence and other properties, observed in the complicated processes of spinel formation and of sintering of the lining materials under actual service conditions, should be quantitatively determined and correctly applied. In the preceding report, the authors illustrated that neural network and FEM can be jointly applied to quantitatively determine the expansion / contraction behaviour due to spinel formation and sintering observed in alumina-magnesia monolithic refractory materials.~6) The combination of neural network and FEM was also applied to the elastic structural analysis of the ladle wear lining, focusing on the spalling process, characterized by thermal stress developed in the lining.~7) The elastic structural analysis, however, is not sufficient for both process analysis and quantitative determination of thermal stress developed in the ladle lining because of the strong non-linearity of the properties of alumina-magnesia monolithic materials used in the steel ladle wear lining. The authors, therefore, first determined the elasto-plastic properties of the materials, then, based on that result, carried out an Elasto-plastic structural analysis of the ladle wear lining.For determination of the elasto-plastic properties of alumina magnesia monolithic refractory materials the conventional load test is not applicable because neither elastic modulus nor plasticity coefficients are correctly calculated from the obtained data, because in the measurements the elastic and the plastic deformation are superimposed. In this study, therefore, the elastic modulus and the plasticity coefficients are calculated by separating elastic and plastic behaviour respectively from the cumulative deformation obtained by iterative loading and unloading.~8) In addition the generalized expansion / contraction neural network, as the model of spinel formation and sintering, is combined with the calculated elasto-plastic properties, to establish the method for the elasto-plastic structural analysis of the ladle wear lining.~8)This paper gives an account of the method to evaluate Elasto-plastic properties of alumina-magnesia monolithic refractory materials, of the transitional Elasto-plastic properties, and of the spalling behaviour based on the structural analysis.
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