Improvement of the Inverse Finite Element Analysis Approach for Tensile and Toughness Predictions by Means of Small Punch TechniqueSource: Journal of Engineering for Gas Turbines and Power:;2021:;volume( 143 ):;issue: 007::page 071008-1Author:Kulawinski, Dirk
,
Iding, Kevin
,
Schornstein, Robin
,
Özdemir-Weingart, Dasgin
,
Dumstorff, Peter
DOI: 10.1115/1.4049900Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: This paper focuses on the inverse finite element analysis (FEA) to calculate the small punch technique (SPT) tests and the prediction of the tensile and fracture toughness behavior. For the description of the SPT tests via FEA, the hardening rule of Ramberg–Osgood (RO) and the damage model of Gursson–Tvergaard–Needleman (GTN) were used. The inverse FEA optimization process cannot provide a unique solution for the 12 parameters included in the material model. This results from a dependency between some parameters, which leads to the same solution in the optimization. Hence, a novel description of the dependent parameters was developed and implemented within the optimization process. Therefore, an enhanced inverse FEA approach was proposed, which provides a fast converging solution for determination of the material model parameters. Within this study, the forged turbine shaft material EN: 27NiCrMoV15-6 was investigated. For comparison purpose, SPT tests as well as tensile tests and fracture toughness tests were carried out. In the case of the tensile properties, the test and simulation show coincidence in the curve and the characteristic values. For the toughness behavior, the characteristic value of the test was met by the simulation.
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| contributor author | Kulawinski, Dirk | |
| contributor author | Iding, Kevin | |
| contributor author | Schornstein, Robin | |
| contributor author | Özdemir-Weingart, Dasgin | |
| contributor author | Dumstorff, Peter | |
| date accessioned | 2022-02-05T22:23:53Z | |
| date available | 2022-02-05T22:23:53Z | |
| date copyright | 3/29/2021 12:00:00 AM | |
| date issued | 2021 | |
| identifier issn | 0742-4795 | |
| identifier other | gtp_143_07_071008.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4277464 | |
| description abstract | This paper focuses on the inverse finite element analysis (FEA) to calculate the small punch technique (SPT) tests and the prediction of the tensile and fracture toughness behavior. For the description of the SPT tests via FEA, the hardening rule of Ramberg–Osgood (RO) and the damage model of Gursson–Tvergaard–Needleman (GTN) were used. The inverse FEA optimization process cannot provide a unique solution for the 12 parameters included in the material model. This results from a dependency between some parameters, which leads to the same solution in the optimization. Hence, a novel description of the dependent parameters was developed and implemented within the optimization process. Therefore, an enhanced inverse FEA approach was proposed, which provides a fast converging solution for determination of the material model parameters. Within this study, the forged turbine shaft material EN: 27NiCrMoV15-6 was investigated. For comparison purpose, SPT tests as well as tensile tests and fracture toughness tests were carried out. In the case of the tensile properties, the test and simulation show coincidence in the curve and the characteristic values. For the toughness behavior, the characteristic value of the test was met by the simulation. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Improvement of the Inverse Finite Element Analysis Approach for Tensile and Toughness Predictions by Means of Small Punch Technique | |
| type | Journal Paper | |
| journal volume | 143 | |
| journal issue | 7 | |
| journal title | Journal of Engineering for Gas Turbines and Power | |
| identifier doi | 10.1115/1.4049900 | |
| journal fristpage | 071008-1 | |
| journal lastpage | 071008-9 | |
| page | 9 | |
| tree | Journal of Engineering for Gas Turbines and Power:;2021:;volume( 143 ):;issue: 007 | |
| contenttype | Fulltext |