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The small punch test (SPT) can be recognized as an alternative to uniaxial tensile testing. Many attempts have been made to extract tensile strength properties from SPT results, but few works on estimating true stress-plastic strain curves. This paper aims to propose an approach for deriving a true stress-plastic strain curve from the force-deflection curve of SPT. Engineering stresses at different plastic strains are extracted from a force-deflection curve using a database method. These engineering stress and plastic strain points are employed to determine the parameters of Ludwik hardening law, then a true stress-plastic strain curve before necking is generated using those parameters. To verify the accuracy in estimating true stress-plastic strain curves in this way, the procedure was applied to three hypothetical materials. It is found that true stress-plastic strain curves before necking are accurately extracted from SPT data. The proposed procedure was also used to process the force-deflection curves of 3Cr1MoV, 2.25Cr1Mo and 16MnR. The results show that the obtained stress and strain relation can predict the force responses of SPT specimens well. Using a database to evaluate true stress-plastic strain curves is effective and with the support of theory that could promote the application of SPT in characterizing material mechanical properties.

Evaluating the mechanical properties of materials of in-service pipelines without shutting down transportation has been always a challenge. A semi-destructive method for determining the true stress-strain curve of in-service natural gas pipeline steel based on backpropagation (BP) artificial neural network and small punch test (SPT) was proposed in this study. The load-displacement curves of 457 groups of different hypothetical materials were obtained by the verified finite element model of SPT within Gurson-Tvergaard-Needleman (GTN) damage parameters and used to train the neural network. The relationship between the load-displacement curve of the SPT and the true stress-strain curve of the conventional tensile test was established based on the trained neural network. The elastoplastic properties of in-service natural gas X80 pipeline steel were obtained by this method. The accuracy and wide applicability of the trained neural network were verified by the experimental results of four types of materials obtained by the SPT and conventional tensile test. This work demonstrates a semi-destructive method, which can be applied to derive the true stress-strain curve of the in-service pipeline steels to determine the elastoplastic properties only by the load-displacement curve of the SPT without performing conventional tensile test.

The indentation technique provides a non-destructive characterization method for in-service equipment. Recent years have seen an increased interest in determination of tensile properties from spherical indentation test (SIT) through inverse optimization theory, while this approach is computational costing and time consuming. This paper aims to propose a novel method for estimation of strength properties based on the application of a database. Firstly, systematically varied Ludwik constitutive parameter vectors were used to establish a database that contains indentation force-depth curves (extracted from the simulated results of indentation test) and corresponding strength values (calculated with the parameter vectors). The indentation force-depth curve of a concerned material was then processed by the database, as a result of which the yield and ultimate strengths can be obtained. A series of virtual materials were used to validate the accuracy of this method. The results show that the database method provides an impressive accuracy and stability in estimating strengths. The database method was subsequently applied to eight real materials. It is found that the predicted strengths agree well with the values from uniaxial tests for most materials, while the database method may be not suitable for materials that show obvious tension-compression asymmetry.