linking icme to component life management during design

Linking ICME to Component Life Management

During Design

Craig McClung, Michael Enright, John McFarland, Kwai Chan

Southwest Research Institute

Wei-Tsu Wu, Ravi Shankar Scientific Forming Technologies Corporation

TMS 2014 Annual Meeting San Diego, California February 17-20, 2014

2

Acknowledgments

• Funding for this effort was provided by US Air Force Research Laboratory

• Small Business Innovative Research (SBIR) Projects – Topic No. AF093-117 – Phase I Contract FA8650-10-M-5110 – Phase II and IIE Contract FA8650-11-C-5105

• Rollie Dutton and Patrick Golden, AFRL Program Monitors Federal Aviation Administration

• Grant 11-G-009 • Joseph Wilson and David Galella, FAA Program Monitors

• Other colleagues made invaluable contributions Jonathan Moody (SwRI) Simeon Fitch (Elder Research) Weiqi Luo, Jinyong Oh (SFTC)

Copyright 2014 Southwest Research Institute

Goals

• The goal of ICME is to optimize materials, manufacturing processes, and component design through integration of computational processes into a holistic system

• The specific goal of this effort is to link manufacturing process simulation directly to a critical measure of component reliability using production software

3

Residual Stresses Microstructure

Material Anomalies

Risk of Component Fracture

Manufacturing Process Simulation Probabilistic Damage Tolerance Analysis


DARWIN® Overview Design Assessment of Reliability With INspection

4 Copyright 2014 Southwest Research Institute

DEFORM – Integrated Process and Material Modeling System

Inertia Welding

Machining

Cogging

Spin Pit Testing

Heat treatment

Forging

Rolling

Furnace Heating

Induction Heating

Extrusion

Sheet Forming

SPF

Hot Press Forming

Spot Welding

Stir Welding

Milling Ring Rolling

Machining Distortion

Life Casting

5

Numerical Simulation of Material Processing


Anomaly Tracking and Deformation Copyright 2014 Southwest Research Institute 6

DARWIN® Overview Design Assessment of Reliability With INspection


• Anomaly location and orientation

• Residual stresses

• Microstructure

DARWIN-DEFORM Links


Anomaly Tracking and Deformation Copyright 2014 Southwest Research Institute 8

DARWIN Stress Superposition Approach for Residual Stresses

• Arbitrary stress gradients are used to calculate crack driving force with weight function stress intensity factors

9

Service StressNeutral file

Residual StressNeutral file

stress gradient

Service Stress

0.0 0.2 0.4 0.6 0.8 1.0-0.8

-0.4

0.0

0.4

0.8

1.2

1.6

2.0

Residual Stress

Combined stress

Residual stress analysisDARWIN Stress ExtractionNormalized Distance

Nor

mal

ized

Stre

ssService StressNeutral file

Residual StressNeutral file

stress gradient

Service Stress

0.0 0.2 0.4 0.6 0.8 1.0-0.8

-0.4

0.0

0.4

0.8

1.2

1.6

2.0

Residual Stress

Combined stress

Residual stress analysisDARWIN Stress ExtractionNormalized Distance

Nor

mal

ized

Stre

ss


Automated Calculation of Crack Growth Life and Risk

• DARWIN can perform full-field automated calculation of location-specific fatigue crack growth life and fracture risk Automatically generate idealized fracture

geometry model for any crack location in an arbitrary geometry

Automatically extract stresses from FE models and calculate stress intensity factors

Automatically calculate FCG lifetime from a common initial crack size at every location

Automatically calculate the risk of component fracture with a probabilistic FCG analysis

• Considering uncertainties in anomaly size & frequency, stress scatter, life scatter, NDE inspection time, and NDE POD



Demonstration Example: Effect of Material Processing Residual Stress on FCG Life

Stress

Life

Without Residual Stress With Residual Stress

11


Effect of Material Processing Residual Stress on Fracture Risk

Life

Without Residual Stress With Residual Stress

Risk

12

Modeling Random Residual Stresses in DARWIN

DEFORM

NESSUS

DARWIN

Stress Results Files

residual stress DOE n contour

residual stress DOE 1 contour

DOE

Gaussian Process Response Surface

Model

Copyright 2014 Southwest Research Institute 13

Demonstration Example: Random Residual Stresses

crack path

crack path


DEFORM Random Variables

Table 1. Application Example Manufacturing Process Parameters

Variable Description Mean Standard Deviation

1 Conv. coeff. factor 0.5 0.167

2 Flow stress factor 0.5 0.167

3 Heat cap. factor 0.5 0.167

4 Object temp 1800.0 8.3

5 Pass 1 offset factor 0.5 0.167

6 Poisson ratio factor 0.5 0.167

7 Therm. con. factor 0.5 0.167

8 Transfer time 25.0 5.0

9 Young mod. factor 0.5 0.167

DOE with 100 residual stress training points using LHS


Principal Components Analysis (PCA) for Residual Stresses Along Crack Path

Training data

Mode shapes


Effect of Random Residual Stress on Risk

Without Residual Stress With Random Residual Stress


DARWIN-DEFORM Links


Anomaly Tracking and Deformation 18 Copyright 2014 Southwest Research Institute

Influence of Forging Strain on Orientation of 3D Anomalies


0.9

1.1

1.3

1.5

1.7

1.9

2.1

2.3

2.3 2.5 2.5

2.7

2.1

1.9 1.9

0.9

1.9 2.1

• Circles represent relative seed area • Lines represent relative major and

minor axis lengths • Angle of major axis is seed orientation

relative to forging

Kantzos et al. 2003, “Effects of Forging Strain on Ceramic Inclusions in a Disk Superalloy,” Adv. Matls and Proc. for Gas Turbines, TMS

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Importing Residual Strain data from DEFORM


Viewing Principal Strain Orientations in DARWIN


Visualizing the Influence of Forging Strains on Anomaly Orientation

First Principal Forging Strain Anomaly Orientation Computed in DARWIN

Note alignment with principal strains


Planned DARWIN Enhancements for Random Anomalies

• Import results from multiple (DOE) DEFORM runs containing residual strain and anomaly occurrence rate information at FE nodes

• Define input random variables associated with DEFORM computations

• Create GP response surface models of residual strain and anomaly occurrence rate

• Response surfaces at initial crack locations only • Compute anomaly occurrence rate scaling factors and

apply to occurrence rates associated with zone anomaly distributions

• Compute random residual strain and anomaly occurrence rate via Monte Carlo simulation

Design of Experiments

Response Surface


DARWIN-DEFORM Links


Anomaly Tracking and Deformation 24 Copyright 2014 Southwest Research Institute

Grain Size Modeling in DEFORM Empirical – JMAK Method

Input: Initial average grain size distribution Strain, temperature, strain rate history Grain growth equations Recrystallization kinetics

• Dynamic • Metadynamic • Static

Output: Location-specific grain size contours Percentage recrystallization

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( ) 1010010

101010 cRTQdad

mnh

drx += /exp.εε

Microstructure-Based Fatigue Crack Growth Model

' 'y f

Esξ4σ ε d

=

( )b

b1/b

EK2sξ

dNda /2

/11

∆= −

∆K: Stress Intensity Range E: Young’s Modulus s: Dislocation Cell Size d: Dislocation Barrier Spacing σy′ : Cyclic Yield Stress εf′: Fatigue Ductility b: Fatigue Exponent D: Grain Size

1/3

00

Dd dD

=


Practical Implementation of Micromechanical Models in DARWIN

• User provides standard fatigue crack growth properties and a single average grain size associated with these properties

• DEFORM calculates average grain sizes at each FE node

• DARWIN computes crack growth rate at selected locations by scaling micromechanical models based on grain size

• A similar paradigm can be used to calculate fatigue crack initiation lifetimes

*

*da D dafdN D dN

=


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Demonstration Example: Influence of Grain Size Scaling on Life & Risk

ANSYS ABAQUS DEFORM

DEFORM

DARWIN

Stress Results

Files

Grain Size Results

File grain size contours

service stress contours


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Influence of Grain Size Scaling on Crack Growth Rate

*

*da D dafdN D dN

=

grain size contours

crack growth rate multiplier

C=1.56 x 10-11

n2=3.66

Nominal values:


Effect of Location-Specific Grain Size Scaling on FCG Life

a=0.01”

Without Grain Size Scaling With Grain Size Scaling

a=0.02”


Effect of Location-Specific Grain Size Scaling on Fracture Risk

Life

Without Grain Size Scaling With Grain Size Scaling

Risk

a=0.01”


Planned DARWIN Enhancements for Random Microstructure

• Import results from multiple (DOE) DEFORM runs containing: Average grain sizes at all finite element nodes ALA grain sizes at selected finite element nodes

• Define input random variables associated with DEFORM computations

• Create GP response surface models of average and ALA grain size Response surfaces for average grain size at all initial crack

locations (all FE nodes) Response surfaces for ALA grain size at initial crack

locations identified by DEFORM (selected FE nodes)

• Compute random average and ALA grain size via Monte Carlo simulation

Design of Experiments

Response Surface


Planned DARWIN Enhancements for Crack Initiation

• Simulate 3D grains based on 6DOF grain information from DEFORM Build GP response surfaces from 6DOF grain results at selected

locations from multiple DEFORM runs containing: • Average & ALA grain sizes & aspect ratios • Grain orientation (Euler angles)

Represent local microstructure as a 3D volume element • Ellipsoid containing grain is simulated using the ALA 3D grain model • Number of facets is sampled from a facet distribution • Characteristics of individual grain boundary facets are sampled from a

misorientation angle distribution


Planned DARWIN Enhancements for Crack Initiation

• Implement enhanced micromechanics-based crack initiation model in DARWIN Formation module Treatment of pile-up length:

• The pile-up length is computed based on the ALA grain size and the misorientation angle of the neighboring grains

• If the misorientation angle is less than a critical value (e.g., 15°), slip across the grain boundary is assumed to occur and the length of the neighboring grain is added to the pile-up length

• This process is repeated until the slipband is blocked by a neighboring grain with a misorientation angle greaten the critical value

Once the pile-up length is determined, the number of fatigue cycles for crack initiation at a slipband and at an inclusion can be computed

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( ) ( )

1/ 2 1/ 22821i

M h cMk ND D

α µσλπ ν ∆ − = −


Planned DARWIN Enhancements for Time-Dependent Crack Growth

• Address concurrent damage mechanisms involving cycle-dependent crack growth due to fatigue and time-dependent crack growth due to corrosion, oxidation, and creep in Ni-based alloys

• Couple microstructure-based time-dependent crack growth models with corresponding cycle-based crack growth model to address effects of long dwell times on component life

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K, MPa(m)1/210 100

da/d

t, m

m/s

ec

10-9

10-8

10-7

10-6

10-5

10-4

10-3

10-2

10-1

100

101

ME3R = 0

649oC

704oC760oC

538oC

816oC

Frequency, Hz10-4 10-3 10-2 10-1 100 101 102

da/d

N, m

m/c

ycle

10-5

10-4

10-3

10-2

10-1

100

204oC

538oC

649oC

704oC ME3, R =0.5∆K = 16.5 MPa(m)1/2

204oC538oC 704oC

649oC


Planned DARWIN Enhancements for Time-Dependent Crack Growth

• Location-specific lifing for a generic ME3 disk: coarse grain size at rim fine grain size at bore mixed grain size in

transition zone.

• Specify tertiary gamma prime size distribution at various disk locations

• Assess the roles of grain size and tertiary gamma size on disk life.

Gayda et al, Superalloys 2004

Gabb et al., Int. J. Fatigue 2011


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Linking Materials and Lifing: Some Specific Needs

• Link microstructure and lifing properties

• Link processing analysis with life analysis

• Probabilistic models linking material/microstructural variability at relevant length scales to variability in fatigue/fracture/life properties and risk

• Microstructure-property models that are computationally efficient and robust Suitable for integration into the overall

optimization process, including linkages to probabilistic lifing codes

Microstructure

Processing

Lifing Properties

Life Prediction

Reliability


Summary

• Interfaces between DEFORM and DARWIN have been developed for full-field, location-specific bulk residual stresses, forging residual strains, and average grain size.

• These interfaces permit full-field results from manufacturing process simulations to be incorporated in predictions of fracture life and reliability.

• Deterministic and probabilistic approaches were presented and demonstrated for modeling the effects of these parameters on crack growth behavior.

• Further work is underway to develop improved deterministic and probabilistic approaches to address microstructural effects on crack initiation and growth.

• The program demonstrates the practical potential for ICME that directly addresses component integrity.


linking icme to component life management during design

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