As the demands on structural systems increase, safety evaluation becomes more and more important, and material characterization at system weak points is essential to assess safety accurately. However, general methods such as uniaxial tensile tests and CTOD (crack tip opening displacement) tests are destructive and thus cannot be applied to systems in use. The instrumented indentation technique was developed to overcome this problem. However, current research on this technique focuses on hardness measurements, and its utility in measuring the flow property, fracture toughness, and residual stress has not been sufficiently evaluated.
In this paper we introduce evaluation methods for flow properties, fracture toughness, and residual stress near a weldment. The algorithm for flow property evaluation was verified by comparison with experimental and FEA (finite element analysis) results. The fracture toughness model was verified by CTOD results, and the residual stress evaluation model was compared with mechanical cutting results.
Reliability studies of in-service materials have recently been concerned with the frequent failure of structural components by time-dependent degradation in severe operating environments, in particular cryogenic contents and many inhomogeneous welded joints. The welded joint is often the initiation point of fracture because of microstructural and mechanical inhomogeneities [1]. Safety diagnoses based on accurate mechanical properties of local regions are indispensable. However, standard tests such as uniaxial tensile tests and fracture mechanics tests, which need bulky standard samples, cannot be used for in-service facilities or for local regions such as welded joints. For example, direct evaluation of mechanical properties of welded joint is mainly limited to microhardness measurements; the indirect method used is testing of a bulky simulated specimen [2]. A new mechanical testing method for in-service welded joints is needed.
In addition, residual stress resulting from welding is one of the most important factors in reliability diagnosis.
