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The spherical indentation test has been successfully applied to inversely derive the tensile properties of small regions in a non-destructive way. Current inverse methods mainly rely on extensive iterative calculations, which yield a considerable computational costs. In this paper, a database method is proposed to determine tensile flow properties from a single indentation force-depth curves to avoid iterative simulations. Firstly, a database that contain numerous indentation force-depth curves is established by inputting varied Ludwic material parameters into the indentation finite elements model. Secondly, for a given experimental indentation curve, a mean square error (MSE) is designated to evaluate the deviation between the experimental curve and each curve in the database. Finally, the true stresses at a series of plastic strain can be acquired by analyzing these deviations. To validate this new method, three different steels, i.e. A508, 2.25Cr1Mo and 316L are selected. Both simulated indentation curves and experimental indentation curves are used as inputs of the database to inversely acquire the flow properties. The result indicates that the proposed approach provides impressive accuracy when simulated indentation curves are used, but is less accurate when experimental curves are used. This new method can derive tensile properties in a much higher efficiency compared with traditional inverse method and are therefore more adaptive to engineering application.

Developing a universal approach for acquiring yield strength and ultimate tensile strength by small punch test (SPT) is a long-standing challenge. In this paper, a methodology is proposed to obtain strength properties of bulk materials from SPT results. It involves extracting true stress–strain curves from SPT data using iterative finite element simulation. Acquired curves show that initial yield stress cannot be reproduced. This is attributed to the inhomogeneous deformation of SPT specimens. Finite element simulations were then conducted on tensile tests with the extracted true stress–strain curves to obtain strength. Results indicate that predicted strength shows a convergent tendency with the times of iterative finite element simulation. An approach is proposed to process the predicted strength, based on data analysis. The acquired strength properties are in good agreement with those obtained by standard tensile testing. This is an entirely novel procedure that predicts reliable tensile properties via a single SPT run. It can be employed with confidence to cases for which standard tensile testing is impractical.

Evaluating the strength properties of materials of an in-service pipeline without shutting down transportation has been always a challenge. A novel and non-destructive method for determining the true stress-strain curve of pipeline steel based on backpropagation artificial neural network and small punch test is proposed in this study. The elastoplastic mechanical properties of the pipeline steels could be obtained by this method. The load-displacement curves of 2261 groups of different hypothetical materials were obtained by the finite element model of small punch test within Gurson-Tvergaard-Needleman (GTN) damage parameters and used to train the neural network. The relationship between the load-displacement curve of small punch test and the true stress-strain curve of the conventional uniaxial tensile test was established based on the trained neural network. The accuracy and wide applicability of the trained neural network were verified by the experimental data of four types of materials obtained by small punch test and standard tensile test, respectively. The established relationship can be used to predict the true stress-strain curve of the pipeline steels to determine the elastoplastic mechanical properties only by the load-displacement curve of the small punch test without performing the conventional tensile test.

Heat exchange tubes are a weak part in reboilers. This paper introduces a cavitation corrosion case of heat exchange tube bundles in an oil reboiler. A series of inspections including chemical, mechanical, energy spectroscopy and metallographic were carried out on heat exchange tube bundles to investigate the material quality. The results show that chemical compositions and mechanical properties of the material agree with requirements. Numerical simulation was performed to figure out the thermal filed of the reboiler heat exchanger. It was found that the temperature of heat exchange tube bundles near support plates was high. The localized high temperature caused boiling bubbles formed faster near support plates than other regions. The impact force induced by bubble collapse lead to cavitation corrosion on the surface of tubes and then produced corrosion pits. The factors causing cavitation corrosion were improper design and operation of the reboiler, and suggestions were given to aviod similar failure ocuured repeatedly.

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.

The GTN model proposed by Gurson, Tvergaard and Needleman has been widely applied to predict ductile fracture. However, the evaluation of the GTN model under high stress triaxiality has only been reported in a few studies. In this paper, a series of tensile tests on round notched specimens were performed to evaluate the applicability of the GTN model parameters under high stress triaxiality. The evaluation was carried out by comparing the predicted load-displacement curves with experimental results. It was observed the GTN model parameters only depend on the material except the critical void volume fraction. The influence of stress triaxiality on the critical void volume fraction was discussed. A further discussion about the construction of a new void coalescence criterion for the GTN model was also presented in this paper.

Damage models are widely used to describe the ductile damage and fracture of metals. This paper proposes a new approach to determine the damage parameters of the Gurson-Tveergard-Needleman model, which uses a load–displacement curve coupled with finite element method. The load–displacement curve was obtained from a tensile test of a smooth tensile specimen and contained information about the damage and fracture behavior of the tested material. The principle of damage parameters identification is to minimize the deviation between experimental and simulated load–displacement curves by a hybrid particle swarm optimization. As a combination of particle swarm optimization and simulated annealing, the hybrid particle swarm optimization is an economical and effective algorithm to identify damage parameters. The identified damage parameters are also verified by testing and simulating deformation shapes of the smooth tensile specimen which is used for parameters determination. Tests and simulations of notched tensile specimens were carried out to discuss the transformability of the identified damage parameters. It is observed that the value of critical void volume fraction of the Gurson-Tveergard-Needleman model decreases with the increase of stress triaxiality.

Failure of spiral wound gaskets rarely occurs due to its outer ring. This paper presented a failure case of a spiral wound gasket owing to the rupture of the outer ring. A series of inspections including chemical, mechanical, metallographic and fractographic analysis were carried out to explore the failure mechanism of that ring. The results show that chemical compositions and mechanical properties of material agree with standard requirements and failure is related to multi-source fatigue fracture. Serious plastic deformation is found near the fracture region. It is owing to squeeze between the bolt and the outer ring, causing a high local circumferential stress. Fatigue fracture occurs when the outer ring is subjected to alternating loads under the high stress. The factors causing fracture of the outer ring were discussed and suggestions were also given for avoiding this failure.

The 17–4 precipitation hardening (PH) stainless steel (SS) is being widely used in high-pressure corrosive environments due to its high strength and excellent corrosion resistance. Some stems of valves employed in petrochemical pipeline are manufactured from 17-4 PH SS in order to ensure strength, impact toughness and reduce corrosion. Here we presented a rare failure case of 17–4 PH SS. In the present failure analysis, an instantaneous fracture of a valve stem in a pipeline system occurred. The stem was made of 17-4 PH SS and ruptured after a short-term use. A systematic study including metallographic, fractographic, chemical and mechanical analysis was carried out to explore the failure mechanism. The results indicate that incorrect peak-aging treatment and phase segregation induce high hardness and high hydrogen embrittlement (HE) tendency of 17-4 PH SS. It was concluded that, the failure of the valve stem is related to HE at the stress concentration region.

Extracting accurate strength properties from small punch test (SPT) results is still a challenge. Previous studies on determining the strength of materials via SPT employed empirical correlations, but these correlations are not generally accurate enough. Here we developed a database method to efficiently evaluate the yield strength and ultimate tensile strength. First, a database containing force-deflection curves of SPT and corresponding strengths of materials is created using finite element simulations coupled with a large amount of hypothetical materials. The database is then used to process the force-deflection curve of an investigated material for estimating strength values. A series of hypothetical materials that are not in the database were adopted to explore the accuracy in determining strengths by this method. The results show the theoretical accuracy values of the yield strength and ultimate tensile strength achieve 92.0% and 99.0%, respectively. The proposed method was also applied to estimate the strengths of Cr-Mo steels. It is found that the obtained strengths agree well with those acquired by uniaxial tensile testing. Using a database to evaluate strength properties is a novel and simple approach that could be widely spread in the study of SPT.