Abstract

Refractory materials are subjected to thermomechanical loads during their working life, and consequent creep strain and stress relaxation are often observed. In this work, the asymmetric high temperature primary and secondary creep behavior of a material used in the working lining of steel ladles is characterized, using uniaxial tension and compression creep tests and an inverse identification procedure to calculate the parameters of a Norton-Bailey based law. The experimental creep curves are presented, as well as the curves resulting from the identified parameters, and a statistical analysis is made to evaluate the confidence of the results.

Highlights

  • Refractory materials, known for their physical and chemical stability, are used in high temperature processes in different industries, such as iron and steel making, cement and aerospace

  • The compression creep curves were already reported by Samadi et al [12], where a different statistical approach was applied for the parameter determination

  • For Sample 2 the test was interrupted after 20 min due to sudden failure of the glue, the resulting creep curve is similar to the one of Sample 3

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Summary

Introduction

Refractory materials, known for their physical and chemical stability, are used in high temperature processes in different industries, such as iron and steel making, cement and aerospace. Ceramics 2020, 3 creep of ceramics are grain boundary sliding, diffusion and dislocation motion [5] This methodology was applied to refractory materials by Martinez et al [6]. The one-dimensional form of the most frequently used model for secondary creep strain rate equation in the context of micromechanical models is [7]: KDGb b p σ n ε =. Phenomenological models attempt to evaluate the effects of creep in a given material regardless of the possible mechanisms that could cause them. This normally results in simpler models with less parameters, at the cost of being less general.

Material
Experiments
Uniaxial Compression Tests
Uniaxial Tensile Tests
Inverse Identification
Statistical Analysis
Results and Discussions
Conclusions
Full Text
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