Investigation of Fracture in Pure Aluminium After Ecap by Means of Small Punch Test

Dymáček, Petr; Dobeš, F.; Král, Pétr; Dvořák, Jiří

doi:10.12776/amsc.v3i0.107

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Conventionally, the following empirical correlation has been used for the estimation of the UTS

σ_{U T S}

from FDCs [17, 21, 35, 77, 96]

σ_{U T S} = β_{U T S} \cdot F_{m} / (h_{0} \cdot v_{m})

with F m being the maximum force, v m the corresponding punch displacement, h 0 the initial specimen thickness, and

β_{U T S}

an empirical coefficient. However, various researchers [9, 77] propose the determination of

σ_{U T S}

based on the force F i instead of F m for the correlation with the UTS in order to avoid a strong dependence of the correlation factor

β_{U T S}

on the tensile properties of the material.…”

Section: An Application Of ML In Materials Property Predictionmentioning

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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“…Conventionally, the following empirical correlation has been used for the estimation of the UTS

σ_{U T S}

from FDCs [17, 21, 35, 77, 96]

σ_{U T S} = β_{U T S} \cdot F_{m} / (h_{0} \cdot v_{m})

with F m being the maximum force, v m the corresponding punch displacement, h 0 the initial specimen thickness, and

β_{U T S}

an empirical coefficient. However, various researchers [9, 77] propose the determination of

σ_{U T S}

based on the force F i instead of F m for the correlation with the UTS in order to avoid a strong dependence of the correlation factor

β_{U T S}

on the tensile properties of the material.…”

Section: An Application Of ML In Materials Property Predictionmentioning

confidence: 99%

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

Stoll

Benner

2021

GAMM-Mitteilungen

View full text Add to dashboard Cite

show abstract

“…The load versus punch displacement curve provides information that allows mechanical properties such as the yield stress or the ultimate tensile strength to be estimated [2][3][4][5][6][7]. Good correlations have been found between SPT parameters and the actual mechanical properties of a wide range of materials, making the SPT a suitable test when structural in-service elements need to be analysed [2][3][4][5]7], or when standard specimens cannot be extracted from small regions of the structure [6,8]. However, it is still a non-standard test, although a European Code of Practice [9,10] Fracture toughness is one of the key properties when analysing structural components, especially components in power applications (neutron-irradiated or thermally-aged materials).…”

Section: Introductionmentioning

confidence: 99%