Integration of Manufacturing Process Simulation with Probabilistic Damage Tolerance Analysis of Aircraft Engine Components

2012 ◽  
Author(s):  
Robert McClung ◽  
Michael Enright ◽  
Wuwei Liang ◽  
Kwai Chan ◽  
Jonathan Moody ◽  
...  
Keyword(s):  
Process Simulation ◽  
Manufacturing Process ◽  
Damage Tolerance ◽  
Tolerance Analysis ◽  
Aircraft Engine ◽  
Damage Tolerance Analysis ◽  
Engine Components

Micromechanics-Based Fracture Risk Assessment Using Integrated Probabilistic Damage Tolerance Analysis and Manufacturing Process Models

2016 ◽  
Author(s):  
Michael P. Enright ◽  
R. Craig McClung ◽  
Kwai S. Chan ◽  
John McFarland ◽  
Jonathan P. Moody ◽  
...  
Keyword(s):  
Grain Size ◽  
Gas Turbine ◽  
Manufacturing Process ◽  
Damage Tolerance ◽  
Random Variables ◽  
Software Tool ◽  
Tolerance Analysis ◽  
Turbine Engine ◽  
Damage Tolerance Analysis ◽  
Time Required

Materials engineering and damage tolerance assessment have traditionally been performed as disjoint processes involving repeated tests that can ultimately prolong the time required for certification of new materials. Computational advances have been made both in the prediction of material properties and probabilistic damage tolerance analysis, but have been pursued primarily as independent efforts. Integrated computational materials engineering (ICME) has the potential to significantly reduce the time required for development and insertion of new materials in the gas turbine industry. A manufacturing process software tool called DEFORM™ has been linked with a probabilistic damage tolerance analysis (PDTA) software tool called DARWIN® to form a new capability for ICME of gas turbine engine components. DEFORM simulates rotor manufacturing processes including forging, heat treating, and machining to compute residual stress and strain, track anomaly location, and predict microstructure including grain size and orientation. DARWIN integrates finite element stress analysis results, fracture mechanics models, material anomaly data, probability of anomaly detection, and inspection schedules to compute the probability of fracture of a gas turbine engine rotor as a function of operating cycles. Previous papers have focused on probabilistic modeling of residual stresses in DARWIN based on manufacturing process training data from DEFORM. This paper describes recent efforts to extend the probabilistic link between DEFORM and DARWIN to enable modeling of residual strain, average grain size, and ALA (unrecrystalized) grain size as random variables. Gaussian Process modeling is used to estimate the relationship among model responses and material processing parameters. These random variables are applied to microstructure-based fatigue crack nucleation and growth models for use in probabilistic risk assessments. The integrated DARWIN-DEFORM capability is demonstrated for a representative engine disk model which illustrates the influences of manufacturing-induced random variables on component fracture risk. The results provide critical insight regarding the potential benefits of integrating probabilistic computational material processing models with probabilistic damage tolerance-based risk assessment.

Role of Quantitative NDE Techniques in Probabilistic Design and Life Management of Gas Turbine Components: Part II

2009 ◽  
Author(s):  
Michael Gorelik ◽  
Alonso Peralta-Duran ◽  
Surendra Singh ◽  
Jonathan Moody ◽  
Michael Enright
Keyword(s):  
Damage Tolerance ◽  
Single Point ◽  
Tolerance Analysis ◽  
Probability Of Detection ◽  
Probabilistic Design ◽  
Life Management ◽  
Damage Tolerance Analysis ◽  
Engine Components ◽  
Life Predictions ◽  
Damage Tolerant

A number of earlier publications discussed the benefits of probabilistic lifing in application to critical rotating engine components. One of the key random variables in both probabilistic and deterministic damage tolerant analysis is a probability of detection (POD) that represents a quantified Non-Destructive Evaluation (NDE) capability. The importance of having accurate POD information and its use in the probabilistic design and life management process is further promoted by the changing customer and regulatory requirements. New and existing FAA / EASA regulations and Air Force standards require damage tolerance analysis to be performed on critical engine components. POD for an NDE inspection is recognized as an important input for damage tolerance analysis. This information is needed for establishing and optimizing inspection schedules, calibrating life predictions, calculating fleet risk etc. Sensitivity of the above results to the accuracy of the POD curve, and to its approximation with a single point value (e.g. 90/50 POD = value of 90% probability with 50% confidence) will be discussed and illustrated with numerical examples. A brief overview of the methods for generating POD curves, and recent advancements in this area will be presented as well.

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