Application of ball punch tests to evaluating fracture mode transition in ferritic steels

McNaney, J.M.; Lucas, G.E.; Odette, G.R.

doi:10.1016/0022-3115(91)90116-o

Cited by 21 publications

(6 citation statements)

References 10 publications

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“…The factor α was found to be 0.32 … 0.34 for 2.25Cr-1Mo steels and 1Cr-0.5Mo steels respectively, based on SP specimens of 0.5 mm thickness and a punch ball diameter of 2.4 mm. However, other results [6,7] give rise to the assumption that the factor α depends on both the SP geometry and the material. Kameda provided a rationale for the linear relationship Eq.…”

Section: Introductionmentioning

confidence: 95%

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Critical evaluation of the small punch test as a screening procedure for mechanical properties

Altstadt

Kuksenko

et al. 2016

Journal of Nuclear Materials

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Within a collective exercise, a systematic and statistically based analysis of the repeatability and device dependence of small punch test results was performed. An unirradiated ferriticmartensitic steel T91 was selected for this. The test results allowed an evaluation of accuracy and reliability of material properties extracted from load-displacement-based and energybased parameters. In a second step, neutron irradiated T91 with doses of 2.3 and 4 dpa was investigated in the temperature range -165 °C to 300 °C. The effects of test temperature and irradiation on the load-displacement curves and on the derived parameters were analysed. It was found that the small punch test is well suited for estimation of neutron embrittlement in cases of small amounts of available material or high activity. The preferred procedure for that purpose was specified.

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Section: Introductionmentioning

confidence: 95%

“…The small punch (SP) test [2][3][4][5][6][7][8][9][10][11] received much attraction and widespread use. However, details of the sample preparation, device and sample geometry as well as testing procedure and analysis may differ from laboratory to laboratory.…”

Section: Introductionmentioning

confidence: 99%

Critical evaluation of the small punch test as a screening procedure for mechanical properties

Altstadt

Kuksenko

et al. 2016

Journal of Nuclear Materials

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“…Bruchhausen et al also proposed a modified approach to this method that normalizes the fracture energies with the maximum force, to produce a clear, constant upper shelf energy and allow for curve fitting using a single function [146]. Sample preparation has been shown to influence results, as specimen surface texture can affect calculated energy levels [56,148]. The use of SPT for determining DBTT, also often denoted as or associated with the fracture appearance transition temperature, FATT, has also been extended for determination of fracture toughness values and fracture behavior evolution in general for evaluation of in-service components in high-stress environments, such as in energy production [23,[149][150][151].…”

Section: Ductile-to-brittle Transition Temperaturementioning

confidence: 99%

Mechanics of the small punch test: a review and qualification of additive manufacturing materials

Torres

Gordon

2021

J Mater Sci

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The small punch test (SPT) was developed for situations where source material is scarce, costly or otherwise difficult to acquire, and has been used for assessing components with variable, location-dependent material properties. Although lacking standardization, the SPT has been employed to assess material properties and verified using traditional testing. Several methods exist for equating SPT results with traditional stress–strain data. There are, however, areas of weakness, such as fracture and fatigue approaches. This document outlines the history and methodologies of SPT, reviewing the body of contemporary literature and presenting relevant findings and formulations for correlating SPT results with conventional tests. Analysis of literature is extended to evaluating the suitability of the SPT for use with additively manufactured (AM) materials. The suitability of this approach is shown through a parametric study using an approximation of the SPT via FEA, varying material properties as would be seen with varying AM process parameters. Equations describing the relationship between SPT results and conventional testing data are presented. Correlation constants dictating these relationships are determined using an accumulation of data from the literature reviewed here, along with novel experimental data. This includes AM materials to assess the fit of these and provide context for a wider view of the methodology and its interest to materials science and additive manufacturing. A case is made for the continued development of the small punch test, identifying strengths and knowledge gaps, showing need for standardization of this simple yet highly versatile method for expediting studies of material properties and optimization.

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“…Various mechanical properties that can be extracted through SPT are for example fracture mode [69, 99, 106], yield stress [21, 70, 84], UTS [59, 96, 105, 165] (based on hydraulic bulge tests which are similar to SPT but high‐pressure hydraulic oil is used instead of punch to cause specimen deformation); Young's modulus [42, 57, 114, 153], fracture toughness [4,18,76,83,95,107,166], creep properties [36, 48], or elastic plastic properties [43, 128].…”

Section: State Of the Artmentioning

confidence: 99%

Machine learning for material characterization with an application for predicting mechanical properties

Stoll

Benner

2021

GAMM-Mitteilungen

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Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data‐driven methods for the discovery of patterns among multiple lengthscales and time‐scales and structure‐property relationships essential. These data‐driven approaches show enormous promise within materials science. The following review covers machine learning (ML) applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of ML methods for material property prediction. Material characteristics such as strength, toughness, hardness, brittleness, or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests, or creep tests are often time consuming and expensive to perform. Therefore, the application of ML approaches is considered helpful for an easier generation of material property information. This study also gives an application of ML methods on small punch test (SPT) data for the determination of the property ultimate tensile strength for various materials. A strong correlation between SPT data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with ML.

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Application of ball punch tests to evaluating fracture mode transition in ferritic steels

Cited by 21 publications

References 10 publications

Critical evaluation of the small punch test as a screening procedure for mechanical properties

Critical evaluation of the small punch test as a screening procedure for mechanical properties

Mechanics of the small punch test: a review and qualification of additive manufacturing materials

Machine learning for material characterization with an application for predicting mechanical properties

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