Improved correlation for elastic modulus prediction of metallic materials in the Small Punch Test

Calaf-Chica, José; Díez, Pedro Miguel Bravo; Calzada, Mónica Preciado

doi:10.1016/j.ijmecsci.2017.10.006

Cited by 45 publications

(24 citation statements)

References 16 publications

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“…This FEM model of the SPT and the goodness of fit for setup calibration to obtain δ upper from δ ext were validated with experimental tests in a previous published research [5]. .…”

Section: Fig 9 Spt Fe Modelmentioning

confidence: 81%

“…1). Research and investigation in the SPT were focused on the evaluation of material properties, including the elastic modulus, yield strength and tensile strength [3][4][5], ductile-brittle transition [6], fracture properties [7][8][9][10], etc. The significant interest shown by researchers in this testing procedure motivated the development of a CEN Code of Practice for the application and use of the small punch test method for metallic materials [11].…”

Section: Introductionmentioning

confidence: 99%

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Development of an improved prediction method for the yield strength of steel alloys in the Small Punch Test

2018

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The Small Punch Test (SPT) is a miniaturized test to characterize the mechanical properties of the materials. The load-displacement curve obtained by this test does not directly provide the material parameters, and linear correlations between data obtained from SPT curve and each mechanical property are necessary. The main difficulty of these correlation methods is the high level of scattering showed when analyzing a wide set of materials in the same study.In this paper, a finite element analysis focused on steel alloys was performed to understand the specimen behavior in the early stages of the SPT. Present methods to correlate the material yield strength with the data obtained from the SPT curve were also analyzed via this FEM study to discover the meaning of the current correlation scattering for this mechanical property. This numerical research also proved the accuracy of the proposed correlation method for the yield strength via the SPT. The maximum slope of zone I (Slope ini ) of the SPT curve showed an accurate correlation with this mechanical property.Focusing on steel alloys, experimental tensile tests and SPT's were performed to validate the numerical analysis and to demonstrate the suitability of the proposed Slope ini versus yield strength correlation method.

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“…This FEM model of the SPT and the goodness of fit for setup calibration to obtain δ upper from δ ext were validated with experimental tests in a previous published research [5]. .…”

Section: Fig 9 Spt Fe Modelmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Development of an improved prediction method for the yield strength of steel alloys in the Small Punch Test

2018

Self Cite

View full text Add to dashboard Cite

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“…Mao correlated the yield load and the maximum load of the SPT with the yield strengths and the ultimate tensile strength of various kinds of materials, and gave the corresponding empirical formula [5]. Chica analyzed the initial stage of the load-displacement curve of the SPT, correlated the slope of the initial stage with the elastic modulus of alloys, and obtained the empirical formula between them [6]. Based on the principle of equivalent energy, Peng [7] analyzed the third stage of load-displacement curve of SPT, and the true stress-strain curve and ultimate tensile strength of the materials were obtained.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Strength Properties of In-Service X80 Pipeline Steel Using Small Punch Test and Backpropagation Neural Network

Song¹,

Li²,

Jiang³

et al. 2021

Preprint

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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.

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“…Other scholars have used small punch tests to test multidirectional behavior, as well. Chica et al studied the elastic modulus of materials based on this experiment [25]. Chica et al studied how to identify the yield parameters of materials based on this experiment [26].…”

Section: Introductionmentioning

confidence: 99%

A Fast Identification Method of Yield Strength of Materials Based on Bending Experimental Data

Duan

Tian

Zhang

et al. 2020

Metals

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Identifying the yield strength of materials quickly and accurately is the key to realizing defect prediction and digital process control on the production line. This paper focuses on identifying the material yield strength based on bending deformation, analyzing the influence of different die fillets, punch fillets, and die spans on the curve shapes, determining the reasonable dimensions of the device, and developing them. Two methods for rapidly extracting the yield load are proposed—the window vector method (WV) and the fitting residual method (FR)—and compared with the double secant line method (CWA) and the one tenth thickness method (t/10). Because there is no direct correspondence between the yield load and the material performance parameters, the relevant equations were fitted using the experimental data. The linear correlation between load and yield strength determined by these four methods was close to 0.99. Finally, four kinds of sheets with high, medium and low yield strength were tested and compared with the observed results. The result shows that when the yield strength is small, the average error and the relevant model dispersion will increase. As the yield strength increases, the biases increase gradually. The prediction errors based on the t/10, WV, and FR methods were all below 4%.

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Improved correlation for elastic modulus prediction of metallic materials in the Small Punch Test

Cited by 45 publications

References 16 publications

Development of an improved prediction method for the yield strength of steel alloys in the Small Punch Test

Development of an improved prediction method for the yield strength of steel alloys in the Small Punch Test

Evaluation of Strength Properties of In-Service X80 Pipeline Steel Using Small Punch Test and Backpropagation Neural Network

A Fast Identification Method of Yield Strength of Materials Based on Bending Experimental Data

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