Modelling of creep in Inconel 706 turbine disc fir-tree

Maharaj, Chris; Morris, A.; Dear, John P.

doi:10.1016/j.msea.2012.08.021

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…In [6], Kim et al estimated the material deterioration of high-temperature components. Maharaj et al studied the finite element simulation of creep behaviour at the coupling of a turbine blade to a turbine disc in a fir-tree region [7]. Lui et al investigated a turbine blade failure due to creep [8] and proposed a numerical approach for assessing the service life of turbine blades, which is based on the Lemaitre-Chaboche creep damage model.…”

Section: Introductionmentioning

confidence: 99%

Creep detection of Hastelloy X material for gas turbine components with nonlinear ultrasonic frequency modulation

Mevissen

Meo

2022

Materials Characterization

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Section: Introductionmentioning

confidence: 99%

Creep detection of Hastelloy X material for gas turbine components with nonlinear ultrasonic frequency modulation

Mevissen

Meo

2022

Materials Characterization

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“…It allows mechanisms and failure modes to be better understood. FEA has been used extensively to model nickel-based superalloys and components (Maharaj et al 2012). Furthermore, FEA and numerical models have been used to model AM microstructure and its evolution (Nie et al 2014;Tan et al 2020), thermal history (Promoppatum et al 2018), process parameter influence on grain morphology (Raghavan et al 2016), meltpool morphology predictions in LPBF alloy 718 (Romano et al 2016) and more.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

et al. 2021

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There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at $$650\,^\circ $$ 650 ∘ C and 600 MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of $$1.40\%$$ 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications.

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“…The working fluid is first evaporated and then passed through an expansion device (for example, a turbine) in which the just now mentioned conversion takes place. In organic Rankine cycles, axial-flow microturbines are usually used as expansion machines [22]. The higher the efficiency of an ORC system, the more popular it can become.…”

Section: Introductionmentioning

confidence: 99%

Stress analysis of the discs of axial-flow microturbines

Bagiński¹,

Zych²,

Żywica³

2020

J. vibroeng.

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The article discusses the mesh creation techniques for models of discs of axial-flow microturbines. A universal method of optimization of such devices, in terms of their strength improvement, has been proposed. The research focused on microturbines that can operate in combination with ORC systems, especially the ones whose discs have many structural components such as pins or chamfers. Calculations were done using the commercial software ANSYS Workbench. Both tetrahedral and hexahedral grids were used in the analysed models. The calculation time needed for the grid preparation was regarded as an important parameter. Therefore, the reference model was created using the disc slice method. The results obtained for the models that included the full complex geometry of the disc were compared with the results obtained for the reference model. The mesh size coefficient was defined. It enabled to simplify the strength optimisation method for discs of axial-flow microturbine and also made it more universal. After carrying out all analyses and computations, it was possible to develop a scheme of conduct during the optimization of the aforementioned expansion devices.

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Modelling of creep in Inconel 706 turbine disc fir-tree

Cited by 11 publications

References 14 publications

Creep detection of Hastelloy X material for gas turbine components with nonlinear ultrasonic frequency modulation

Creep detection of Hastelloy X material for gas turbine components with nonlinear ultrasonic frequency modulation

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

Stress analysis of the discs of axial-flow microturbines

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