Fatigue life prediction on nickel base superalloys
Fatigue life prediction on nickel base superalloys
Neural networks have been used extensively in material science with varying success.It has been demonstrated that they can be very effective at predicting mechanical properties such as yield strength and ultimate tensile strength. These networks require large amounts of input data in order to learn the correct data trends. A neural network modelling process has been developed which includes data collection methodology and subsequent filtering techniques in conjunction with training of a neural network model.It has been shown that by using certain techniques to ‘improve’ the input data a network will not only fit seen and unseen Ultimate Tensile Strength (UTS) and Yield Strength (YS) data but correctly predict trends consistent with metallurgical understanding.Using the methods developed with the UTS and YS models, a Low Cycle Fatigue (LCF) life model has been developed with promising initial results.
Crack initiation at high temperatures has been studied in CMSX4 in both air and vacuum environments, to elucidate the effect of oxidation on the notch fatigue initiation process. In air, crack initiation occurred at sub-surface interdendritic pores in all cases.The sub-surface crack grows initially under vacuum conditions, before breaking out to the top surface. Lifetime is then dependent on initiating pore size and distance from the notch root surface. In vacuum conditions, crack initiation has been observed more consistently from surface or close-to-surface pores - indicating that surface oxidation is in-filling/”healing” surface pores or providing significant local stress transfer to shift initiation to sub-surface pores. Complementary work has been carried out using PWA1484 and Rene N5. Extensive data has been collected on initiating pores for all 3alloys. A model has been developed to predict fatigue life based upon geometrical information from the initiating pores. A Paris law approach is used in conjunction with long crack propagation data. The model shows a good fit with experimental data and further improvements have been recommended in order to increase the capability of the model.
Miller, Mark
ccb4087e-97ff-43d2-81d9-e8ab1d38f843
July 2007
Miller, Mark
ccb4087e-97ff-43d2-81d9-e8ab1d38f843
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Miller, Mark
(2007)
Fatigue life prediction on nickel base superalloys.
University of Southampton, School of Engineering Sciences, Doctoral Thesis, 237pp.
Record type:
Thesis
(Doctoral)
Abstract
Neural networks have been used extensively in material science with varying success.It has been demonstrated that they can be very effective at predicting mechanical properties such as yield strength and ultimate tensile strength. These networks require large amounts of input data in order to learn the correct data trends. A neural network modelling process has been developed which includes data collection methodology and subsequent filtering techniques in conjunction with training of a neural network model.It has been shown that by using certain techniques to ‘improve’ the input data a network will not only fit seen and unseen Ultimate Tensile Strength (UTS) and Yield Strength (YS) data but correctly predict trends consistent with metallurgical understanding.Using the methods developed with the UTS and YS models, a Low Cycle Fatigue (LCF) life model has been developed with promising initial results.
Crack initiation at high temperatures has been studied in CMSX4 in both air and vacuum environments, to elucidate the effect of oxidation on the notch fatigue initiation process. In air, crack initiation occurred at sub-surface interdendritic pores in all cases.The sub-surface crack grows initially under vacuum conditions, before breaking out to the top surface. Lifetime is then dependent on initiating pore size and distance from the notch root surface. In vacuum conditions, crack initiation has been observed more consistently from surface or close-to-surface pores - indicating that surface oxidation is in-filling/”healing” surface pores or providing significant local stress transfer to shift initiation to sub-surface pores. Complementary work has been carried out using PWA1484 and Rene N5. Extensive data has been collected on initiating pores for all 3alloys. A model has been developed to predict fatigue life based upon geometrical information from the initiating pores. A Paris law approach is used in conjunction with long crack propagation data. The model shows a good fit with experimental data and further improvements have been recommended in order to increase the capability of the model.
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M.Miller_Thesis_Final_Version.pdf
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Published date: July 2007
Organisations:
Engineering Mats & Surface Engineerg Gp
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Local EPrints ID: 68693
URI: http://eprints.soton.ac.uk/id/eprint/68693
PURE UUID: 1e6e660d-7806-4333-ac23-12b6ad2dbd51
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Date deposited: 16 Sep 2009
Last modified: 14 Mar 2024 02:37
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Author:
Mark Miller
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