Abstract

The main tasks of the small punch (SP) technique are to derive standard material properties from a limited amount of material, including strength/toughness, creep and fatigue behaviour etc. The complicated problem of determining fracture toughness poses significant difficulties. Although many methods have been put forward to increase the prediction accuracy, no satisfactory methods are available yet. Recently, the so-called “Local approach” has been introduced to determine ductile fracture from the small punch test by means of a micro-mechanical model introduced by Gurson and modified by Needleman and Tvergaard. By using the observed load-deflection curve from the small punch test, reverse finite element analysis is performed to identify both the material plastic property (σ-ε curve) and the damage parameters of the Gurson model. With these material parameters known, a standard fracture toughness (CT) specimen is simulated by finite element analyses, from which the J-integral and the crack extension can be estimated. A J-R resistance curve can be created by multiple specimens with different load-line-displacements, and the fracture toughness JIC can be determined according to ASTM E 1820-17.In order to verify this new approach, the standard uniaxial test, the small punch tests and the standard fracture toughness tests are needed with the same material. Up to now, only a few of these kinds of experimental verification are available. In this paper, an overview on the existing methods of determination of fracture toughness from small punch test is given, and the “Local approach” for small punch test is addressed. Verifications are summarized from three high level institutes, including MMT in the Czech Republic, JRC Petten in the Netherlands and EPRI in the USA. The predicted JIC with that from standard fracture toughness tests are compared. A good agreement is found and shows that the “Local approach” is capable of predicting fracture toughness from small punch tests. The prediction accuracy by “Local approach” is better than existing methods. Further work is needed as available tests are still limited.

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