Identification of plastic properties of metallic structures by artificial neural networks based on plane strain small punch test
| Autor: | Mohammad Ehsan Hassani, Wenke Pan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
Artificial neural network business.industry Strategy and Management 02 engineering and technology Structural engineering 01 natural sciences 010101 applied mathematics Identification (information) 020303 mechanical engineering & transports 0203 mechanical engineering Load displacement 0101 mathematics Safety Risk Reliability and Quality business Plane stress |
| Zdroj: | International Journal of System Assurance Engineering and Management. 8:646-654 |
| ISSN: | 0976-4348 0975-6809 |
| DOI: | 10.1007/s13198-017-0617-5 |
| Popis: | In order to assess the strength of aged and in service components, small punch test (SPT) has emerged. However, it has two disadvantages, firstly using of the hemispherical punch which is difficult to manufacture in most conventional workshops and secondly the known difficulties in obtaining the flat disk samples. This paper discusses a novel approach, the plane strain small punch test to identify the plastic properties of metallic structures. To do so, a new apparatus was designed and manufactured to perform a series of plane strain SPT in room temperature. An artificial neural network was established and trained by the corresponding load displacement responses obtained from the simulations to predict the plastic properties of Stainless Steel 304L. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink