Surface integrity investigations for prediction of fatigue properties after machining of alloy 718
•Predictive fatigue life models surface integrity investigations and fatigue testing.•The models are to be used when selecting between different manufacturing alternatives.•The models combines topography, residual stress and deformation. Fatigue performance is crucial for gas turbine components, and...
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| Published in | International journal of fatigue Vol. 144; p. 106059 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.03.2021
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| Subjects | |
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| Abstract | •Predictive fatigue life models surface integrity investigations and fatigue testing.•The models are to be used when selecting between different manufacturing alternatives.•The models combines topography, residual stress and deformation.
Fatigue performance is crucial for gas turbine components, and it is greatly affected by the manufacturing processes. Ability to predict the expected fatigue life of a component based on surface integrity has been the objective in this work, enabling new processing methods.
Alloy 718 samples were prepared by different machining setups, evaluated in fatigue testing and surface integrity investigations. These results generated two predictive statistical multi-variate regression models.
The fatigue correlated well with roughness, residual stresses and deformation. The two models showed great potential, which encourages further exploration to fine-tune the procedure for the particular case. |
|---|---|
| AbstractList | •Predictive fatigue life models surface integrity investigations and fatigue testing.•The models are to be used when selecting between different manufacturing alternatives.•The models combines topography, residual stress and deformation.
Fatigue performance is crucial for gas turbine components, and it is greatly affected by the manufacturing processes. Ability to predict the expected fatigue life of a component based on surface integrity has been the objective in this work, enabling new processing methods.
Alloy 718 samples were prepared by different machining setups, evaluated in fatigue testing and surface integrity investigations. These results generated two predictive statistical multi-variate regression models.
The fatigue correlated well with roughness, residual stresses and deformation. The two models showed great potential, which encourages further exploration to fine-tune the procedure for the particular case. Fatigue performance is crucial for gas turbine components, and it is greatly affected by the manufacturing processes. Ability to predict the expected fatigue life of a component based on surface integrity has been the objective in this work, enabling new processing methods. Alloy 718 samples were prepared by different machining setups, evaluated in fatigue testing and surface integrity investigations. These results generated two predictive statistical multi-variate regression models. The fatigue correlated well with roughness, residual stresses and deformation. The two models showed great potential, which encourages further exploration to fine-tune the procedure for the particular case. Fatigue performance is crucial for gas turbine components, and it is greatly affected by the manufacturing processes. Ability to predict the expected fatigue life of a component based on surface integrity has been the objective in this work, enabling new processing methods. Alloy 718 samples were prepared by different machining setups, evaluated in fatigue testing and surface integrity investigations. These results generated two predictive statistical multi-variate regression models. The fatigue correlated well with roughness, residual stresses and deformation. The two models showed great potential, which encourages further exploration to fine-tune the procedure for the particular case. © 2020 The Authors |
| ArticleNumber | 106059 |
| Author | Holmberg, Jonas Berglund, Johan Hammersberg, Peter Wretland, Anders Suárez, Alfredo Beno, Tomas |
| Author_xml | – sequence: 1 givenname: Jonas surname: Holmberg fullname: Holmberg, Jonas email: jonas.holmberg@ri.se organization: RISE Research Institutes of Sweden AB, Argongatan 30, 431 53 Mölndal, Sweden – sequence: 2 givenname: Anders surname: Wretland fullname: Wretland, Anders email: anders.wretland@gknaerospace.com organization: GKN Aerospace Engine System Sweden AB, 461 81 Trollhättan, Sweden – sequence: 3 givenname: Peter surname: Hammersberg fullname: Hammersberg, Peter email: peter.hammersberg@chalmers.se organization: Chalmers University of Technology, 412 96 Gothenburg, Sweden – sequence: 4 givenname: Johan orcidid: 0000-0003-3656-1806 surname: Berglund fullname: Berglund, Johan email: johan.berglund@ri.se organization: RISE Research Institutes of Sweden AB, Argongatan 30, 431 53 Mölndal, Sweden – sequence: 5 givenname: Alfredo surname: Suárez fullname: Suárez, Alfredo email: alfredo.suarez@tecnalia.com organization: Tecnalia R&T, Mikeletegi Pasealekua, 7, E-20009 Donostia-San Sebastián, Spain – sequence: 6 givenname: Tomas surname: Beno fullname: Beno, Tomas email: tomas.beno@hv.se organization: University West, Production Technology, 461 86 Trollhättan, Sweden |
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| Snippet | •Predictive fatigue life models surface integrity investigations and fatigue testing.•The models are to be used when selecting between different manufacturing... Fatigue performance is crucial for gas turbine components, and it is greatly affected by the manufacturing processes. Ability to predict the expected fatigue... |
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| SubjectTerms | Alloy 718 Component based Fatigue of materials Fatigue performance Fatigue prediction Fatigue properties Fatigue testing Machining Manufacturing process Non-conventional machining Petroleum prospecting Processing method Production Technology Produktionsteknik Regression analysis Regression model Surface integrity |
| Title | Surface integrity investigations for prediction of fatigue properties after machining of alloy 718 |
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