Neuro-finite element application in material characterization using small punch test
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Krishna K. Pathak
Abstract
Estimation of accurate in-service life is of great importance to the power generating industries, especially thermal and nuclear power plants. Since only a small amount of material is available for testing purposes, a miniature test is found to be of immense utility. In this study, six hundred and sixty finite element simulations of the small punch test were carried out considering different material and frictional parameters. Based on these results, five neural network models were developed. Successfully trained artificial neural networks were used to predict flow properties and yield stress. The artificial neural network results were finally validated with the experimental results and the two most suitable models were selected. The proposed approach offers a powerful reverse engineering tool for material characterization.
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- Original Contributions
- Neuro-finite element application in material characterization using small punch test
- Multi-phase biocomposite material in-situ fabricated by using hydroxyapatite and amorphous nanosilica
- Particularities of the formations of bainite and martensite/austenite phase in low carbon low alloy steels during continuous cooling
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- Deformation behaviour of freestanding single-crystalline Ni3Al-based nanoparticles
- Precipitation strengthening in high manganese austenitic TWIP steels
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- Enhancement in optical transmission of ZnO: Al film by c-orientation arrayed growth
- Physical and mechanical behavior of high strength self-compacting concrete containing ZrO2 nanoparticles
- Experimental and numerical studies of metallic powders subjected to cold isostatic pressing
- Consumption of Cu pad during multiple reflows of Ni-doped SnAgCu solder
- Nanocrystalline CdS thin films prepared by sol-gel spin coating
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- Professor Dr.-Ing. Jürgen Haußelt zum 65. Geburtstag
- DGM News
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