Probabilistic methods for design assessment of reliability with inspection

Wu, Y.-T.; Enright, Michael P.; Millwater, Harry

doi:10.2514/3.15143

Cited by 32 publications

(32 citation statements)

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“…In addition, proper sampling strategies can be employed to achieve a desired sampling accuracy result for a given confidence interval, thereby focusing on variables that should have the most effect on risk reduction. 62 While probabilistic approaches have been used successfully in previous work for predicting thresholds for cracking in commercial aluminum alloys, 63,64 such approaches would also appear to be useful for modeling/predicting the effects of changes in AM process variables on subsequent performance as well as modeling location-specific properties.…”

Section: Probabilistic Modeling Of Fracture and Fatiguementioning

confidence: 99%

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Overview of Materials Qualification Needs for Metal Additive Manufacturing

Seifi

Salem

Beuth

et al. 2016

JOM

501

183

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This overview highlights some of the key aspects regarding materials qualification needs across the additive manufacturing (AM) spectrum. AM technology has experienced considerable publicity and growth in the past few years with many successful insertions for non-mission-critical applications. However, to meet the full potential that AM has to offer, especially for flightcritical components (e.g., rotating parts, fracture-critical parts, etc.), qualification and certification efforts are necessary. While development of qualification standards will address some of these needs, this overview outlines some of the other key areas that will need to be considered in the qualification path, including various process-, microstructure-, and fracture-modeling activities in addition to integrating these with lifing activities targeting specific components. Ongoing work in the Advanced Manufacturing and Mechanical Reliability Center at Case Western Reserve University is focusing on fracture and fatigue testing to rapidly assess critical mechanical properties of some titanium alloys before and after post-processing, in addition to conducting nondestructive testing/evaluation using micro-computerized tomography at General Electric. Process mapping studies are being conducted at Carnegie Mellon University while large area microstructure characterization and informatics (EBSD and BSE) analyses are being conducted at Materials Resources LLC to enable future integration of these efforts via an Integrated Computational Materials Engineering approach to AM. Possible future pathways for materials qualification are provided.

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Section: Probabilistic Modeling Of Fracture and Fatiguementioning

confidence: 99%

“…2). There has been increased use of probabilistic methods for design assessment of reliability with inspection (DARWIN) 62 for a variety of processing techniques (e.g., casting, forging, heat treatment, etc.) to deal with local variations in microstructure/ defects/properties in lifing estimates.…”

Section: Probabilistic Modeling Of Fracture and Fatiguementioning

confidence: 99%

Overview of Materials Qualification Needs for Metal Additive Manufacturing

Seifi

Salem

Beuth

et al. 2016

JOM

501

183

View full text Add to dashboard Cite

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“…One viable alternative is reliability-based fatigue life prediction approach, which does consider the uncertainties through the probabilistic-based model to predict the probability distribution of fatigue life to overcome the shortages of deterministic analysis. In view of these virtues, reliability-based fatigue life prediction has been widely used to account for the uncertainties of materials and structures, including reliability-based crack growth assessment [11][12][13], probabilistic strain-life fatigue modelling [14][15][16], probabilistic analysis for creep-fatigue behavior [17], physics of failure-based fatigue life prediction accounting for model uncertainty [18], low cycle fatigue life prediction under material variability [19,20], and probabilistic life assessment using mature commercial software tools [21][22][23][24]. From the aforementioned studies, the uncertainty factors in fatigue life prediction have been adequately explored, and the feasibility and effectiveness of the reliability-based fatigue life prediction were validated as well.…”

Section: Introductionmentioning

confidence: 99%

Reliability‐Based Fatigue Life Prediction for Complex Structure with Time‐Varying Surrogate Modeling

Song

Bai

Fei

et al. 2018

Advances in Materials Science and Engineering

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To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. By integrating the proposed space-filling Latin hypercube sampling technique and PSO-GRNN regression function, the mathematical model of TV/PSO-GRNN is studied. The reliability-based fatigue life prediction framework is illustrated in respect of the TV/PSO-GRNN surrogate model. Moreover, the reliability-based fatigue life prediction of an aircraft turbine blisk under multiphysics interaction is performed to validate the TV/PSO-GRNN model. We obtain the distributional characteristics, reliability degree, and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO-GRNN, and TV/PSO-GRNN, we observe that the TV/PSO-GRNN surrogate model is promising to perform the reliability-based fatigue life prediction of the turbine blisk and enhance the computational efficiency while ensuring an acceptable computational accuracy. The efforts of this study offer a useful insight for the reliability-based design optimization of complex structure.

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“…To better assess the risk of disk failure, a probabilistic fatigue crack growth (FCG) methodology has been previously developed for treating fretting fatigue by considering the stress variability [1], inspection intervals [2], material variability [2], and risk mitigation by locally-induced residual stresses [3,4]. The methodology is based on an integration of a global finite element analysis of the disk-blade assembly [5] and associated contact stresses [6 ,7], small-crack fretting fatigue modeling [7], and risk assessment using the DARWIN (Design Assessment of Reliability with Inspection) probabilistic fracture mechanics code [8]. The methodology has been demonstrated for an actual military engine disk under real life loading conditions.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic High-Cycle Fretting Fatigue Assessment of Gas Turbine Engine Components

Chan

Enright

Golden

et al. 2011

Volume 6: Structures and Dynamics, Parts a and B

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High-cycle fatigue (HCF) is arguably one of the costliest sources of in-service damage in military aircraft engines. HCF of turbine blades and disks can pose a significant engine risk because fatigue failure can result from resonant vibratory stresses sustained over a relatively short time. A common approach to mitigate HCF risk is to avoid dangerous resonant vibration modes (first bending and torsion modes, etc.) and instabilities (flutter and rotating stall) in the operating range. However, it might be impossible to avoid all the resonance for all flight conditions. In this paper, a methodology is presented to assess the influences of HCF loading on the fracture risk of gas turbine engine components subjected to fretting fatigue. The methodology is based on an integration of a global finite element analysis of the disk-blade assembly, numerical solution of the singular integral equations using the CAPRI (Contact Analysis for Profiles of Random Indenters) and Worst Case Fret methods, and risk assessment using the DARWIN (Design Assessment of Reliability with Inspection) probabilistic fracture mechanics code. The methodology is illustrated for an actual military engine disk under real life loading conditions.

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Probabilistic methods for design assessment of reliability with inspection

Cited by 32 publications

References 0 publications

Overview of Materials Qualification Needs for Metal Additive Manufacturing

Overview of Materials Qualification Needs for Metal Additive Manufacturing

Reliability‐Based Fatigue Life Prediction for Complex Structure with Time‐Varying Surrogate Modeling

Probabilistic High-Cycle Fretting Fatigue Assessment of Gas Turbine Engine Components

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