Predicting stress and creep life in Inconel 718 blade-disk attachments

Saberi, Ehsan; Nakhodchi, Soheil; Dargahi, Ashkan; Nikbin, Kamran

doi:10.1016/j.engfailanal.2019.104226

Cited by 16 publications

(10 citation statements)

References 36 publications

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“…(3) and (4), respectively. (Liu and Murakami, 1998;Saberi et al, 2020). The experimental data agreed with the described LM model and the listed constants, as shown in Figs.…”

Section: Determination Of Creep Constantssupporting

confidence: 83%

“…where ̇ and are the creep strain rate and deviatoric stress in the multiaxial state, respectively. The constants and can be obtained through curve fitting with the uniaxial creep data from the Norton model (Liu and Murakami, 1998;Saberi et al, 2020). The damage parameter is defined as the range between 0 and 1 for no damage and full failure, respectively.…”

Section: Creep Constitutive Equationsmentioning

confidence: 99%

“…Over the last four decades, theoretical creep models have been developed to comprehensively explain experimental creep data (Cane, 1982;Murakami, 2012;Murakami and Liu, 1995). The combination of the Norton (Norton, 1929) and damage-coupled Liu-Murakami (LM) models (Liu and Murakami, 1998) could be used to determine creep properties and have recently been used for different material and structural applications (Saberi et al, 2020;Doi et al, 2020). When the material experiences creep deformation under constant stress at a given temperature, it reaches failure through three different evolution steps: primary, secondary and tertiary regimes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of creep behavior using a damage-coupled model for martensitic stainless steel

Yoon

Kimura

Toku

et al. 2021

Mechanical Engineering Journal

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In this study, the Liu-Murakami (LM) creep damage-coupled model was considered to evaluate the creep properties of martensitic stainless steel. The degree of creep damage was examined at two temperatures (565 ℃ and 593 ℃) to assess mechanically and thermally activated processes. A series of high applied stresses (applied stress/ultimate strength 0.5) was considered for accelerated creep loadings. A full set of creep constants was determined by combining the Norton and LM models. Constitutive equations were used to quantitatively estimate experimental creep curves. The variation in creep constants was discussed based on stress sensitivity, such as stress triaxiality and applied stress, depending on the power of stress. The creep strain-time curves were successfully estimated. The comparison between the experimental and analytical results was in good agreement in the tertiary regime. In addition, the compensation of the two applied temperatures provides a supplementary explanation of the relationship between the ultimate strength and rupture time in terms of temperature sensitivity. The analytical results show that different applied stresses and temperatures could be compensated to characterize the creep behavior of the material. Thus, the creep strain-time and creep strain rate-certain rupture time curves were finally achieved. The analytical process in this study provides a laboratory-scale assessment of creep properties using the accelerated creep test and LM model.

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“…(3) and (4), respectively. (Liu and Murakami, 1998;Saberi et al, 2020). The experimental data agreed with the described LM model and the listed constants, as shown in Figs.…”

Section: Determination Of Creep Constantssupporting

confidence: 83%

Section: Creep Constitutive Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of creep behavior using a damage-coupled model for martensitic stainless steel

Yoon

Kimura

Toku

et al. 2021

Mechanical Engineering Journal

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“…Nickel-based superalloy with nickel as the matrix, adding W, Mo, Ti, Al, Nb, Co and other strengthening elements, has excellent high-temperature thermal strength, creep resistance and high-temperature fatigue strength [ 1 ]. The material is widely applied to fabricate structural components [ 2 ] in the aerospace field of 900~1050 °C temperature, such as aero-engine blades [ 3 , 4 , 5 ], turbine discs [ 6 ], combustors [ 7 ], etc. Its excellent chemical and metallurgical properties are mainly reflected in the nickel base alloy being able to dissolve a variety of alloying elements and maintain good structural stability.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Strain Fatigue Behavior for Inconel 625 with Laser Shock Peening

Sun

Han

et al. 2022

Materials

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With excellent creep resistance, high-temperature thermal strength and high-temperature fatigue strength, Inconel 625 is widely applied to fabricate structural components in the aerospace field, where fatigue life is a key point. Laser shock peening (LSP) is considered to improve the fatigue strength and fatigue crack growth resistance of metal materials. The present work was conducted to investigate the influence of LSP on strain-controlled fatigue behavior of Inconel 625. The surface microstructures of specimens before and after LSP were observed by transmission electron microscope (TEM). The strain-controlled fatigue loading tests with different strain amplitudes ranging from 0.4% to 1.2% were carried out on the specimens, and the topography of fracture appearance was examined by scanning electron microscope (SEM). The investigations showed that the specimens with LSP presented fewer crack initiations, shorter fatigue striations space and smaller dimples or micropores, which account for the enhancement of the fatigue life for the LSP specimens. Furthermore, the plastic deformation, ultra-fine grains, twins and dislocations caused by LSP could prevent crack initiation, crack propagation and ultimate fracture, hence prolonging the fatigue life of the Inconel 625. In addition, it was revealed that the cyclic strain hardening as well as cyclic strain softening remains almost the same to Inconel 625 with or without LSP.

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“…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. However, FEA can be complex, computationally expensive and over/under estimate stresses (Saberi et al 2020).…”

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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Predicting stress and creep life in Inconel 718 blade-disk attachments

Cited by 16 publications

References 36 publications

Assessment of creep behavior using a damage-coupled model for martensitic stainless steel

Assessment of creep behavior using a damage-coupled model for martensitic stainless steel

Investigation of Strain Fatigue Behavior for Inconel 625 with Laser Shock Peening

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

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