a database for documenting full scale fatigue test and ... files/weds/1000...results of current and...

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Copyright © 2005 The Boeing Company. All rights reserved.

A Database for Documenting Full Scale A Database for Documenting Full Scale Fatigue Test and Fleet Teardown Fatigue Test and Fleet Teardown

FindingsFindings

Rigo Perez, Cynthia Rose and Tom Fellner

2005 ASIP ConferenceMemphis, Tennessee

29 November – 1 December 2005

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• F/A-18E/F Full-Scale Fatigue Tests

• Database Development

• Database Application

Full-Scale Fatigue Test Teardown Database

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New Forward Fuselage Test

FT76

New Wing Test FT77

Full-Scale Fatigue Test FT50

F/A-18E/F Full-Scale Fatigue Tests

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FT50 Background

• Tested to 18,000 FH• 2,600 Cracks in 300 Parts

Original WingBlock 1 Fwd Fuselage

Center/Aft Fuselage Represents Current Production

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• Tested to 12,000 FH• Static Test• 64 Cracks Detected

FT76 Background

Redesigned ForwardFuselage

Previous Test ArticleUsed as Fixture

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Teardown DatabaseFT77 Background

TE Flap FT78

TEF Shroud FT78

I/W - FT77

Aileron FT78

O/W - FT77

Aileron Shroud FT78

Wing skins not shown for structure visibility.

ILEF – FT77

OLEF – FT77

New Wing fold & LEF Transmissions – FT77

FT78 TEF:- Two Lifetimes Maneuver- Less Than One Lifetime Buffet

4th Lifetime:Buffet

4th Lifetime:Wingfold Bending Moment

Stretch Tested

Static TestMidboard Pylon

All Structure Tested to at Least Three LifetimesExcept TEF

All Structure Tested to at Least Three LifetimesExcept TEF

Wing Cost Reduction Design

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Teardown Database Development

• Accommodate all teardown and inspection results of current and future Boeing fatigue test articles

• Suitable for use before, during, and after the physical teardown and inspection of a test article

• Provide data for life extension programs

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Teardown Database

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Teardown DatabasePlanning

• Teardown and inspection tasks– Define parts requiring inspections– Define level of inspection for each part

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Teardown DatabasePlanning

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• Results entered as they become available

• Status of the teardown and inspections

Teardown DatabaseActive Tasks

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Teardown DatabaseInspection Results

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Teardown DatabaseInspection Results

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Teardown DatabaseLab Failure Analysis Levels

• L1 – optical microscope – determine failure mode & crack origin

• L2 – scanning electron microscope – look for flaw at crack origin & determine suitability for L3

• L3 – develop crack growth curve by measuring fracture surface features

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Teardown DatabaseLab Failure Analysis Levels

FT77 Lower Wing Splice Fitting

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Teardown DatabaseLevel 1 Failure Analysis

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Teardown Database Level 2 Failure Analysis

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Teardown DatabaseLevel 3 Failure Analysis

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Teardown DatabaseInspection Result Report

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Teardown DatabaseInspection Status Report

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Teardown DatabaseAfter Teardown

• Permanent documentation of inspection results

• Includes results of all inspected parts, regardless of crack status

• Information available to perform teardown analysis

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Teardown DatabaseApplication –F/A-18A-D SLAP

SLAP (Service Life Assessment Program) uses full-scale fatigue test results and fleet usage/fatigue tracking data to assess remaining service life of the F/A-18A-D fleet

• Several retired a/c are undergoing structural teardown

• Teardown data will provide service life info for verifying SLAP analysis

• Database will document teardown findings

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• Large amount of data generated by three full-scale fatigue tests and several component tests

• Database provides electronic storage of fatigue test findings – easy entry & retrieval

• Database expanding to include fleet teardown as part of the F/A-18A-D SLAP

Teardown DatabaseSummary

Database Development for Destructive Evaluation and Extended Fatigue Testing of

Retired Aircraft Fuselage Structure

David SteadmanTechnical Operations Center

Delta Air Lines

John BakuckasAirworthiness Assurance Branch

FAA William J. Hughes Technical Center

2005 USAF Aircraft Structural Integrity Program ConferenceMemphis, TN November 29 - December 1, 2005

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Research Partners

• FAA William J. Hughes Technical Center

• Delta Air Lines

• Drexel University

• Airworthiness Assurance Nondestructive Inspection Validation Center (Sandia)

Partners in the Destructive Evaluation and Extended Fatigue Testing of a Retired Transport Aircraft.

Acknowledgement:This work was supported by the Federal Aviation Administration, Airworthiness Assurance Branch, AAR-480, located at the William J. Hughes Technical Center, Atlantic City International Airport, New Jersey.

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Presentation Outline

• Program Overview

– Research Objectives

– Program Components

• Database Specifics

– Tabular Data Structure

– Graphical Data Structure

– Design for Distribution

• Interface Demonstration

• How to Join the Beta Test

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Research Objectives

• The objectives of this research program are:– to characterize the state of multiple site damage (MSD) in retired fuselage

structure that has reached its Design Service Goal – to advance the state of MSD through extended fatigue testing – to assess the capabilities of Non-Destructive Inspection (NDI) methods– to develop and validate analysis methods to predict the state of MSD at any

point in time• The data generated from this effort will be used:

– to provide better understanding of the fatigue behavior of real structure– to provide better understanding of the capabilities and limitations of existing

and emerging NDI technologies– to calibrate and validate widespread fatigue damage (WFD) assessment

methods. – to develop a knowledge base within the FAA on experimental procedures,

analytical methods, and data reduction approaches for use in assessing programs to preclude WFD

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Research Objectives

PROJECT GOALS

• Advance the knowledge base within FAA and industry on WFD related issues:

– MSD initiation, growth, and failure

– NDI capability

– Teardown and data reduction protocols

– Validity of WFD assessment analytical tools

• Results of this work will be applicable to current and soon-to-be aging aircraft types.

NOT PROJECT GOALS

• Goals listed are potential drivers for a teardown program, but are not goals of this program.

– Assess the current state of an aircraft model fleet

– Develop specific fleet inspection programs

– Validate stress magnitudes, service spectrum, or mission mix assumptions

– Develop service life assessment and extension programs

The database design, as with all teardown, analysis and testing tasks, is driven by the stated goals of the research.

The Electronic Database provides the core "Factual

Report" for this project

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• Structure Selected - removal of WFD susceptible structure from a retired transport airplane at Design Service Goal.

• Damage Characterization - to characterize the state of MSD in the fuselage structure.

• Inspection Assessment - to assess the capabilities of existing and emerging NDI methods to detect MSD.

• FASTER Testing - to advance the state of MSD in selected sections through extended fatigue testing.

• Data Analysis - to develop analysis methods that can predict the state of MSD at any point in time, and are validated with Characterization and FASTER results.

Program Components

Objectives of the Electronic Database• Capture relational data gathered under the Inspection, Characterization, and Testing tasks• Support Data Analysis through query and filtering tools• Allow distribution of collected data to FAA and industry

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Structure Selected

Panels Removed

S- 4R

S –4L

FS 360 FS 720 FS 1130FS 760

Extended Fatigue Testing

Destructive Evaluation

• Airplane: – Model: 727-232– Serial Number: 20751– Line Number: 1000

–Cert. Issue Date: 01-21-74– Flight Cycles: 59,494– Flight Hours: 66,412– Retired: 10/29/98– No inspections per AD 99-04-22

• Fuselage Structure:– Lap - Joints along Stringer 4R and 4L– Over 100 NDI crack indications – 11 Panels removed

–7 Teardown Inspection–4 Extended Fatigue Testing

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Damage Characterization

Typical stereo-micrograph of faying surface showing multiple cracks

Fractograph mosaic of fracture surface

Data collected includes:• Tabular data from crack measurements

– Crack lengths, orientations, and directions– Rivet installation– Hole quality– Striation spacing

• Graphics from microscopy– Prior to fastener removal– Faying surface– Fracture surface at varying magnification (macro to

striation)

Objective: Characterize the state of MSD in fuselage structure of a retired transport airplane at Design Service Goal. Focus was large amount of MSD found in the lower row, lower skin of the longitudinal lap joint. Developed protocol for teardown evaluation including an approach to disassemble joints and expose crack surfaces for fractographic examinations.

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Ultrasonic NDT for tearstrap delamination using B727 NDT procedures in outdoor conditions, prior to a/c disassembly.

Lower row S-4R cracks as depicted by an emerging NDT method - external LFEC on SAIC automated scanner

Inspection Capability Assessment

Objective: assess the capabilities of standard and emerging NDI methods to detect MSD.

Standard NDI Methods• Detailed Visual Inspection• Internal Mid Frequency Eddy Current• External Low Frequency Eddy Current

Emerging NDI Methods• 17 state-of-the-art NDI methods evaluated• All Emerging methods are external inspections• Subject of a separate ASIP presentation (Piotrowski,

et al)

Data collected includes:• Hit/Miss tabular data• Screen capture graphics• Documentation photographs

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Extended Fatigue Testing

• FASTER – FAA’s Full-Scale Aircraft Structural Test Evaluation and Research Facility

– Fixture applies internal pressure, plus hoop, longitudinal, and shear loads to curved panels

– Monitor and record crack development using high magnification visual techniques and NDI at regular intervals

• Each test will provide:

– MSD initiation and growth rates.

– Critical MSD distribution when FAR's not met.

– Cycles from conservative analytical failure criteria to actual failure.

• Data collected within the database includes

– Cycles vs crack length and strain gauge measurements

– Crack photographs taken from remote cameras

– NDI inspection results (hit/miss and graphics)

Objective: Advance the state of MSD in selected sections through extended fatigue testing.

FT2 test panel installed. Remote narrow FOV and wide FOV external cameras shown. Remote internal underwater camera is also in use

Schematic with panel installed, including shear fixture

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Database Structure

The database structure and user interface were designed to:1. Capture relational data gathered under the Inspection, Characterization, and

Testing tasks2. Support Data Analysis through query and filtering tools3. Allow distribution of collected data to FAA and industry

These objectives are typical for a teardown conducted by a small engineering organization, even if their Teardown Goals are not strictly research:

1. Relational structure is essential to link data from different areas, potentially importing tables from different companies.

2. Focus is on data organization and retrieval, rather than data entry. Assumes that data will be entered by a small group, and that the data entry interface can be simple.

3. Distribution is important if the data will be submitted for certification. Allowance for distribution also means that the database can be archived easily after the teardown is complete.

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Current Volume in the Database

• 12,960 Locations

– 23 Inspection Result Fields (Standard and Emerging)

– 210 Locations with Fractography Data

• 4,031 Photographs

– 730 NDT Screens

– 3,301 Stereomicroscope and SEM Fractographs

• 506 Striation Count Locations (2 data points per location)

• Above does not include data from characterization of the test panels or measurements from FASTER testing, which is still in progress.

Maintaining Relationships and Filtering Data are the critical

database function

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Two Types of Data

Two broad types of data captured:

• Tabular Data

– crack lengths, inspection findings, rivet parameters, crack growth rates, etc.– stored in collection of record tables.– volume and data relationships primarily affect structural complexity

• Graphical data – photographs, stereo-micrographs, SEM micrographs, NDT screen captures

and response plots, etc.– stored as graphic files (e.g., *.jpg, *.tif)– number and required resolution primarily affect storage space requirements

and portability - 4 GB of data anticipated.

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Tabular Data Structure

• Location Level

– ShortCode

• Crack Level

– ShortCode

– Crack No.

• Striation Level

– ShortCode

– Crack No.

– Striation No.

Data record keys are defined at each of three data levels:

Basic data on a large number of locations (e.g., NDI results, fastener measurements)(e.g., NDI results, fastener measurements)

Moderate volume of data on the locations with cracks(e.g., crack length, faying surface conditions, a fewphotos)

Large volumeof data on selected cracks. (e.g. fracture

surface examination, striation counts, many photo’s)

>12,920 Locations>1,300 S-4 Lower Row Holes

196 MFEC Indications> 150 Cracks w/Teardown

>50 Cracks with SEM

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Tabular Data Structure

Location Level - applicable to single hole

• Hole Quality and Drilling Defects

• Faying Surface Defects

• Rivet Installation

• Unlimited number of cracks allowed at each location

Striation Level

• Striations/inch

• Distance from Crack Origin

Crack Level - applicable to single crack

• Up to 3 Crack Length Measurements

• Crack Orientation and Direction

• Number and Location of Crack Origins

• Unlimited number of striation counts allowed on each fracture surface

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Standard Naming Scheme

• Location Level

– ShortCode

• Crack Level

– ShortCode

– Crack No.

• Striation Level

– ShortCode

– Crack No.

– Striation No.

Data record keys are defined user standard naming schemes at each of three data levels:

x

y(xi, yi)

Increasing Crack Number

FWD

UP

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Standard Naming Scheme

• Location Naming Scheme has benefits compared a traditional mapping method

– Each hole has a unique address, so it is easy to add new locations to the teardown plan and database.

– Consistent use of this address by different groups responsible for the inspections, hole characterizations, and analysis streamlines comparisons later.

– It is intuitive, at least to researchers close to the program:

• Researchers can communicate about a certain hole without referring to a “hole numbering map” or some other reference document.

• Typographical errors are less likely, as the location name has meaning.

• It is easier for a second researcher to double-check results.

• Similar system could be readily developed for any semi-monocoque structure. More difficult for large fittings.

• The flexibility inherent in this system is particularly advantageous to a small engineering group.

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Graphical Data Structure

• Because of their size, all graphics are stored outside the database (CD's or PC's hard drive).

• Database tables link graphic file's path to location tables.

• Photographs associated with 1 of 2 levels

– Most are location level: stereo-micrographs, SEM, most NDI.

– Some are frame bay level:

• Emerging NDI like MWM, MAUS, digital and film x-ray, etc.

• Documentation photographs of skin/stringer structure

• Important that a location query return all location and frame bay graphics.

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Designed for Distribution

• Microsoft Access chosen as the database application– Microsoft Access is a common PC application included in MS Office

Professional that interacts well with MS Word and MS Excel.– Access has powerful tools to create forms, queries, and reports to work with

large amounts of data.• Standard queries, parametric studies, and reports will filter data as needed

– Expertise in MS Access will not be required for use.– Knowledgeable MS Access users will be able to design very sophisticated

custom data queries.• Intended to be distributed as a CD/DVD collection. Disk 0 contains the Access

database and pdf reports, and remaining disks contain the graphics. The current database is 45 MB, plus over 2.2 GB in graphics).

• Excepting the large volume of graphic files, it is well suited to web-based distribution as well.

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Standard Queries

Note: since the database is still under development, all interface forms and reports should be considered preliminary

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Create Report From Records

Simplest query:• combines Characterization and Inspection data• returns all location-level data, plus the maximum crack size, for the selected area

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Create Report From Records

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Create Parametric Study

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Create Parametric Study

-90

-70

-50

-30

-10

10

30

50

70

90

0 0.05 0.1 0.15 0.2 0.25

Crack Length (inches)

Cra

ck D

irect

ion

(deg

rees

)

Creates table of parameters, intended for import into spreadsheet such as MS Excel for further analysis.

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Find Photographs - Query Tool

Returns all graphics (photographs, NDI, etc):• From within an area of interest - frame bay or specific location• Constructs a Boolean query on the graphics' captions

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Find Photographs - Query Tool

• Graphics files are returned in an interface form, or can be printed in a report.

• Graphics are sequenced in an intuitive order by caption

• Frame bay, then location• NDI screens, from Standard to Emerging• Stereo fractographs, low magnification to

high• SEM fractographs, origin to crack tip

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View Documentation

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Beta Test Evaluation

• Test Script Topic Areas:

– Create a Report of Inspection and Fractography Records

– Conduct a Parametric Study

– Find Photographs Using the Query Tool

• Rating Criteria for Feature Usefulness :

– Must Have: A Feature I Will Use Extensively

– Feature I Want

– Feature I Might Use

– Feature I Probably Do Not Need

• Rating Criteria for Ease of Use :

– Very Intuitive: No Problems encountered in Usage

– Interface is Acceptable

– Interface Needs Improvement

– Interface and/or Feature Does not Work

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Tasks and Schedule

• Alpha Testing By In-House Team - in progress

• Introduce Database and Solicit Testers

– ASIP 2005, December, 2005

– FAA/DoD/NASA Joint Conference on Aging Aircraft, March 2006

• Finalize Beta Test List, April 2006

• Beta Test Program, May to July 2006

• Incorporate Suggestions and Fix Bugs, November 2006

• Release Final Database, December 2006

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Volunteer as a Beta Tester

To volunteer as a beta tester, contact:

Dr. John Bakuckas

Structural Integrity Research Manager

Airworthiness Assurance Branch, AAR-480

FAA William J. Hughes Technical Center

Atlantic City International Airport, NJ 08405

[email protected]

(609) 485-4784

Teardown - An Aid to the Assurance of Continuing Structural Integrity for UK Military Aircraft Programmes

TEARDOWN – AN AID TO THE ASSURANCE OF CONTINUING STRUCTURAL INTEGRITY FOR UK

MILITARY AIRCRAFT PROGRAMMES

2005 USAF Structural Integrity Program Conference

Memphis, TN

30 November 2005

M J Duffield (QinetiQ, Farnborough, UK)&

C Hoyle (BAE SYSTEMS, Chadderton, UK)


TEARDOWN – AN AID TO THE ASSURANCE OF CONTINUING STRUCTURAL INTEGRITY FOR UK

MILITARY AIRCRAFT PROGRAMMES

PART 1 – WHY TEARDOWN?

PART 2 – TEARDOWN OF RETIRED SERVICE AIRFRAMES

PART 3 – TEARDOWN OF A FATIGUE TEST ARTICLE


WHY TEARDOWN?

UK MILITARY AIRWORTHINESS REGULATIONS (JSP 553)•ANNEX K – STRUCTURAL INTEGRITY MANAGEMENT

– Conduct structural sampling and teardown– Scheduled plan required once aircraft declared ageing– Use to be made of Cat 3, 4 and 5 aircraft

UK MILITARY AVIATION ENGINEERING POLICY AND REGULATION (JAP 100A-01)

•CHAPTER 11.1.3 – VALIDATING STRUCTURAL INTEGRITY– “A teardown is defined as a progressive, detailed, controlled and destructive examination

of an aircraft structure. There are 2 forms of teardown; the teardown of a full-scale fatigue test specimen airframe at the conclusion of testing and the teardown of an ex-service aircraft.”


PART 2 - TEARDOWN OF RETIRED SERVICE AIRFRAMES


THE VC10 IN RAF SERVICE

•CMk1K – Military variant of VC10 long-range jet airliner – includes underwing air-to-air refuelling pods – 14 examples produced, 10 currently in service

•KMk2 – Ex-commercial Standard VC10 converted to 3-point AAR tanker – 5 examples produced, all retired from service by 2003

•KMk3 – Ex-commercial Super VC10 converted to 3-point AAR tanker – 4 examples produced, all remaining in service

•KMk4 – Ex-commercial Super VC10 converted to 3-point AAR tanker (but minus additional fuel cells and cargo door of KMk3) – 5 examples produced, 2 remaining in service


TEARDOWN OF RETIRED SERVICE AIRFRAMESEXAMPLES

• MAIN WING JOINTS AT RIB 0 AND RIB 22

• FIN SPAR ATTACHMENTS TO REAR FUSELAGE


MAIN WING JOINTS AT RIB 0 AND RIB 22

• “FAIL-SAFE” JOINTS – BUT DAMAGE TOLERANCE DIFFICULT TO PROVE

•NO RELIABLE FATIGUE TEST RESULTS

•POTENTIALLY SUSCEPTIBLE TO MULTIPLE-SITE FATIGUE DAMAGE

THE PROBLEM:



THE SOLUTION:

TEARDOWN OF SAMPLE JOINTS FROM 3 KMK2 AND 1 KMK4 AIRCRAFT

OVER 1500 BOLT HOLES EXAMINED WITH JOINTS FULLY DISMANTLED



THE RESULTS:

•NO SIGNIFICANT FAULTS FOUND – NO EVIDENCE OF MSD

•KNOWN SERVICE LIVES OF RETIRED AIRFRAMES ANALYSED TO PRODUCE TEARDOWN-VALIDATED INSPECTION THRESHOLD FOR REMAINING FLEET

•THRESHOLD IN EXCESS OF PLANNED RETIREMENT FOR ALL REMAINING CMk1K/KMk3/KMk4 AIRCRAFT

•BREAKDOWN OF INTERFAY SEALANTS AND PROTECTIVE TREATMENTS– WIDESPREAD CONTAMINATION BY MOISTURE AND FUEL– POTENTIAL LONG-TERM CORROSION ISSUE


FIN SPAR ATTACHMENTS TO REAR FUSELAGE

THE PROBLEM:• CORROSION EVIDENT ON EXTERIOR OF JOINT ELEMENTS – PENETRATION WITHIN JOINTS NOT KNOWN

• SAMPLE BOLTS REMOVED FROM RETIRED AIRFRAME – SUBSTANTIAL CORROSION EVIDENT

• SERVICE INVESTIGATION NECESSITATED THE REMOVAL OF THE FIN – NOT A ROUTINE PROCEDURE!

THE SOLUTION:• TEARDOWN OF SEVERAL SAMPLES FROM REDUNDANT KMK2 FLEET


FIN SPAR ATTACHMENTS TO REAR FUSELAGE

THE RESULTS:• CORRELATION BETWEEN BORE DAMAGE AND CORROSION WITHIN JOINTS ESTABLISHED

• NO EVIDENCE OF FATIGUE CRACKING

• OVERSIZING OF BOLT HOLES SHOWN TO BE EFFECTIVE IN REMOVING SURFACE CORROSION IN BORES

• COST-EFFECTIVE MAINTENANCE REGIME– SYSTEMATIC REPLACEMENT

OF BOLTS – NO NEED TO REMOVE FIN

CORROSION OF STEEL BOLT

BEFORE

AFTER


PRACTICAL ASPECTS OF TEARDOWN

• TEAMWORK

• DOCUMENTATION & MANAGEMENT

• RECOVERY OF SAMPLES

• STORAGE OF SAMPLES

• LABORATORY ACTIVITIES– DISASSEMBLY– NDT– POST-TEARDOWN DISPOSAL

• TRACKING OF SAMPLES


TEAMWORK

AIRCRAFT DESIGN AUTHORITY (BAE SYSTEMS)

AIRFRAME STRUCTURAL INTEGRITY ADVICE TEAM (QINETIQ)

AIRCRAFT RECOVERY & TRANSPORTATION FLIGHT (RAF)

STRUCTURAL INTEGRITY WORKING GROUP (JOINT MOD/INDUSTRY)

VC10 INTEGRATED PROJECT TEAM (MOD)

NON-DESTRUCTIVE EVALUATION TEAM (RAF)

MATERIALS & STRUCTURES GROUP (RAF)

TEARDOWN

TEARDOWN TEAM (QINETIQ)

VC10 STRUCTURAL AIRWORTHINESS ADVISORY TEAM


DOCUMENTATION & MANAGEMENT

AIRCRAFT RECOVERY & TRANSPORTATION FLIGHT (RAF)

VC10 INTEGRATED PROJECT TEAM (MOD)

TEARDOWN

TEARDOWN TEAM (QINETIQ)

VC10 STRUCTURAL AIRWORTHINESS ADVISORY TEAM

COSTED PROPOSAL

AUTHORITY TO PROCEEDSTATEMENT

OF WORK

TECHNICAL REQUIREMENT

DRAWINGS

DIGITAL IMAGES

SAMPLES

WORK PACKAGES


RECOVERY OF SAMPLES

•NO HANGAR FACILITIES AVAILABLE

•TIMESCALE DICTATED BY RE-DEVELOPMENT SCHEDULE OF AIR BASE

•DEPENDENT UPON AVAILABILITY OF RAF AR&TF TEAM

•QINETIQ TEARDOWN TEAM LOCATED 150 MILES FROM SITE


STORAGE OF SAMPLES

•AIRFRAME BROKEN DOWN INTO MAJOR ELEMENTS

•LOCAL RECOVERY OF REQUIRED SAMPLES

•SAMPLES TRANSPORTED TO STORAGE FACILITY PENDING TEARDOWN

•SAMPLES ORGANISED, LABELLED AND AUDITED TO ENSURE TRACEABILITY


LABORATORY ACTIVITIES

• INITIAL INSPECTION (INCLUDING NDT)•DIS-ASSEMBLY AS SPECIFIED BY STATEMENT OF WORK•VISUAL & NDT INSPECTIONS AT EACH STAGE OF DISASSEMBLY


LABORATORY ACTIVITIES

•DEFECT ANALYSIS•REPORTING

Figure 9. Fatigue striations observed on fracture surface of aft crack at hole 13.


TRACKING OF SAMPLES

• SAMPLE NUMBERING SYSTEM• CUT LINES• LABELLING

– PAINT– HIGH VISIBILTY TAPE– INDELIBLE MARKERS

SAMPLE NO. 144/A(1)

(NOTE WEATHERING OF ORIGINAL CUT LINES AND LABELLING)


TRACKING OF SAMPLES

•DIGITAL IMAGING•VIBRO-ETCHING OF COMPONENTS•DRAWINGS

– PART NOS.– PART DESCRIPTIONS– MODIFICATION STANDARD


TEARDOWN OF RETIRED SERVICE AIRFRAMESSUMMARY OF VC10 PROGRAMME

•7 AIRFRAMES AVAILABLE DUE TO FLEET RATIONALISATION

•OPPORTUNITY USED TO PROVIDE TEARDOWN EVIDENCE TO SUPPORT DAMAGE TOLERANCE QUALIFICATION OF “FAIL-SAFE” DESIGN

•TEARDOWN OF FLEET LEADERS ALSO USED TO ADDRESS AGEING AIRCRAFT CONCERNS

•OVER 100 SAMPLES TORN DOWN ENCOMPASSING MOST SIGNIFICANT STRUCTURAL FEATURES, PARTICULARLY JOINTS

•RESULTS USED TO VALIDATE, MITIGATE OR DICTATE ENGINEERING ACTIVITY TO ENSURE CONTINUING STRUCTURAL AIRWORTHINESS


TEARDOWN OF A FATIGUETEST ARTICLE


PURPOSE OF TEARDOWN

•CHECK FOR PREVIOUSLY UNDETECTED DEFECTS

•SUPPORT SUBSTANTIATION OF THE RESIDUAL STRENGTH TEST

•AID DEVELOPMENT OF IN-SERVICE INSPECTIONS


SELECTION CRITERIA FOR STRUCTURE

•WING DESIGN PHILOSOPHY

•IN-SERVICE FLEET EXPERIENCE

•RELEVANT TEST EVIDENCE

•36 SIGNIFICANT STRUCTURAL AREAS IDENTIFIED


WING DESIGN PHILOSOPHY

•INNER WINGS AND CENTRE SECTION ARE TWO SPAR MASS BOOM STRUCTURES

•INBOARD RIB 3 SPARS CARRY WING SHEAR, BENDING AND TORSION BY DIFFERENTIAL BENDING

•OUTBOARD RIB 3 SPANWISE LOADING CONCENTRATED TOWARDS THE SPARS FROM THE TWO MULTI-STRINGER BOXES FORE AND AFT OF THE MAIN UNDERCARRIAGE WHEEL WELL

•WING DESIGN INBOARD OF RIB 7 IS SAFE LIFE IN CONCEPT


FLEET EXPERIENCE

•SIGNIFICANT CORROSION REPAIRS READACROSS TO THE FTS

•INCORPORATED ONTO THE PORT WING AS SIMULATED BLENDS AND REPAIRS

•INTEGRITY OF THESE REPAIRS TO BE ESTABLISHED BY TEARDOWN


FATIGUE TEST RESULTS

•ALL DEFECTS WITHIN PRIMARY STRUCTURE ARISING FROM FATIGUE CYCLING TO BE EXAMINED


INSPECTION REQUIREMENTS

•TIERED INSPECTION SCHEDULE DETERMINED

•Visual - Level 1 (VI) consisting of an internal and external examination of unstripped structure

•Visual - Level 2 (V2) consisting a detailed close up with x10 magnification examination of structure with paint, sealant removed

•Visual - Level 3 (V3) consisting of a specialised detailed examination using probes, boroscopes etc


INSPECTION REQUIREMENTS contd

•Bond Testing

•High Frequency Eddy Current

•Rotating High Frequency Eddy Current

•Magnetic Flaw Detection

•Penetrant Flaw Detection


REPORTING PROCEDURE

•All parts examined to be individually referenced and documented to identify location and orientation

•All damage found to be recorded on damage log format similar to the FTS report but prefixed with a `T’

•A digital photograph required of all component parts with damage

•Monthly internal meetings with MEAT Lab and Structures Department

•Quarterly meetings with the RAF Customer and his advisors (review of six assemblies per time)


Nimrod Wing FTS Teardown Inspection

Assembly No.21

Identification:21P4

Description: Rib 5 Portion Bottom Boom – RWWWto Rear Spar

Techniques used / Settings V1 V2 V3 PFD MFD HFEC REC BONDPlease circle

Photographs

File No. / name Location Showing21P4#1 Outboard side of Bottom Boom. Location of cracks.

21P4#2,3 Flanges on Boom. Close up of cracked areas.

21P4#4 Inboard surface of Boom. Close up of Crack A.

21P4#6 Fwd surface of fwd flange. Close up of Crack C.

21P4#8 Lower surface of middle flange. Close up of Crack E.


FINDINGS

•Only two areas contained findings of such significance that has prompted the need for new NDT fleet inspections

•Another two areas contained defects that have required new directed visual fleet inspections

•Another four areas contained defects that have required enhancedinspections to be included within existing Engineering Instructions


CONCLUSION

•FOLLOWING SUCCESSFUL COMPLETION OF THE TEARDOWN EXERCISE THE NIMROD MR2 WING CAN BE PROVIDED WITH THE FULL 100 FI QUALIFICATION

Fleet fatigue life interpretation from full-scale and coupon fatigue tests - a

simplified approach

S. Barter, M. McDonald and L. MolentASIP05

S. Barter M. McDonald L. Molent

Background

• Royal Australian Air Force (RAAF) require validated crack growth tools to aid in airframe lifing (F/A-18 lifing considered here).

• Tools should be accurate and not overly conservative and address “spectrum effects” and the differences noted between Quantitative Fractography (QF) results and conventional LEFM model results.


F/A-18 Lifing Procedure• RAAF uses guidance from UK Def. Standard 970: apply test factor on full-scale test result of

about 3 with no failure up to the residual strength (1.2xDDL) at the end of the test, and in-service monitoring.

• Buffet loaded structure leads to scatter/test factors > 3.0.

• Blind prediction requires additional safety factors.

• Unmonitored in-service structure requires additional safety factors.

• Comparing damage caused by the test spectrum with the service spectrum can be difficult requiring additional safety factors.

• Full-scale testing is rarely carried out beyond 3 lives leaving interpretation problems.

• We need to maximise the value of full-scale fatigue tests without increasing their duration.

• For this we need accurate reliable crack growth (CG) prediction tools.


The Requirement

• Ability to:• Accurately extrapolate beyond test crack lengths.• Predict cracking under the same load spectrum at different

stresses.• Compare different spectra of the same type. • Predict changes in the crack growth caused by

geometry/load shedding/residual stress.


Current tools available• LEFM predictive programs: AFGROW, FASTRAN…..• Strain life predictive programs: CI89……….• FEM.• Test life results: Full-scale, components and coupon. • Teardown results.• Crack growth measurement results taken during and

after testing.• Surface length measurements.• Quantitative Fractography (QF) of crack surfaces.

• QF appears to be under utilized!


QF – Why do it?

QF is a powerful tool used regularly at DSTO to interpret service, full-scale and coupon test failures.QF can be used to:

• Produce data about where and from what a crack initiated.

• Define the loading state that produced the cracking.• Determine how fast cracks were growing.

QF can be used to assess the life of the component with this type of cracking.


QF has revealed some generalities:Variable amplitude loaded full-scale tests and in service critical cracks in fighter aircraft have the following features:

• Start growing shortly after the start of loading.• Start from material discontinuities.

• Most cracks start from discontinuities at or very near the surface.• Most of the life of the crack is spent when the crack is less than 1mm

deep• It is often possible to track the crack growth back to very small crack sizes

(about 0.01mm).• The fastest cracks grow in a consistent exponential fashion from very

small sizes to the on-set of tearing.


Example: AL7475-T7451 - Wing root bending moment spectrum

• Historical USAF data.• Complex F-16 500hr

loading spectrum.• Data suggests

exponential growth.• Data suggests

cracking started very early in these test.

Speaker S M, Gordon D E, Kaarlela W T, Meder A, Nay R O, Nordquist F C and Manning S D. (1982) Durability method development, volume VIII – Test and fractography data. Air Force Flight Dynamics Laboratory, Wright-Patterson Air Force Base, AFFDL-79-3118.

Note Initial & Final crack sizes

0.01

0.1

1

10

100

0 5000 10000 15000 20000 25000 30000

AFLR4 No.1 32KSI Bore AFLR4 No.2 32KSI Bore AFLR4 No.3 32KSI Bore AFLR4 No. 4 32KSI AFLR4 No. 6 32KSIAFLR4 No. 9 32KSIAFLR4 No. 10 32KSI AFLR3 No. 2 32KSI AFLR3 No. 4 32KSI Bore AFLR3 No. 3 32KSI Bore AFMR4 No. 2 34KSI AFMR4 No. 4 34KSI AFMR4 No. 5 34KSI AFMR4 No. 7 34KSI AFMR4 No. 10 34KSI AFMR4 No. 11 34KSI AFMR4 No. 13 34KSI Bore AFMR4 No. 14 34KSI Bore AFMR4 No. 33 34KSI Bore AFMR4 No. 36 34KSI Bore

Simulated Flight Hours


Example: D6ac Steel - Wing root bending moment spectrum

• Historical USAF data• Simple F-16 400hr

loading spectrum.• These data suggests

mostly exponential growth

0.001

0.01

0.1

1

10

0.0001

0.001

0.01

0.1

0 2000 4000 6000 8000 10000 12000 14000

D6ac 400hr spec

SFLP4 No.1 100KSI Corner BoreSFLP4 No.2 100KSI CornerSFLP4 No.3 100KSI BoreSFLP4 No.4 100KSI Bore (Near Corner)SFLP4 No.5 100KSI BoreSFLP4 No.6 100KSI BoreSFMP4 No.2 110KSI BoreSFMP4 No.1 110KSI BoreSFMP4 No.3 110KSI BoreSFMP4 No.4 110KSI CornerSFMP4 No.5 110KSI Bore (Near Corner)SFHP4 No.1 125KSI CornerSFHP4 No.2 125KSI CornerSFHP4 No.3 125KSI BoreSFHP4 No.4 125KSI BoreSFHP4 No.5 125KSI Bore

SFH


These Results Lead to a General Model of Crack Growth:

a ≈ a0e (ψ.life) proposed by Frost & Dugdale 1958

ψ is a stress/geometry/material factor that is ≈constant per block for “similar geometry”

Assumptions:

No load shedding or residual stresses changes

• Tearing is ignored as only a small fraction of total life in a fighter aircraft will occur after its onset.

• Crack growth commences shortly after intro into service

Shown to apply for a large class of problems

• Typical critical crack size ≈ 10mm• Typical initial crack size ≈ 0.01mm

log a

life

log ao

Known Points in Crack Growth Life


Why the need to project beyond the fatigue test?• The DEFSTAN includes addition

factors on the test life • These multiply the typical 31/3rd

safety factor applied to the test result,

• They account for such issues as buffet, structure that will not be monitored (for loads) in service, or structure certified by a safe S-N analysis alone (without testing).

• The USAF structural impairment criterion and the USN crack initiation criterion have been included for comparison

For Optimised Structures

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1

10

0 2 4 6 8 10

Typical DEFSTAN test - Safe lifeTypical DEFSTAN test + no monitoring in serviceTypical DEFSTAN test + buffet factorTypical DEFSTAN Safe S-N analysis aloneUSAF crack growth analysisUSAF durability testDamage tolerance analysisUSN durability test - Safe life

Design service life

Typical maximum test life


Extrapolation of test crack depths• Why not use LEFM prediction tools such as

AFGROW or FASTRAN?• Calibrate AFGROW against full-scale, component

or coupon lives.

• Problems arise!


Typical AFGROW predictions

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10

0 10000 20000 30000 40000 50000 60000 70000

Time, t (SFH)

Cra

ck le

ngth

, a (m

m)

KS1G9 (324.1 MPa)KS1G3 (324.1 MPa)KS1G36 (324.1 MPa)KS1G54 (324.1 MPa)KS1G66 (324.1 MPa)KS1G48 (324.1 MPa)KS1G18 (358.5 MPa)KS1G46 (358.5 MPa)KS1G41 (358.5 MPa)KS1G32 (358.5 MPa)KS1G69 (358.5 MPa)KD1R13 (396.5 MPa)KD1R23 (396.5 MPa)KD1E10 (396.5 MPa)KD1R12 (396.5 MPa)KD1P14 (396.5 MPa)KD1P24 (396.5 MPa)KS1G29 (396.5 MPa)KS1G14 (428.9 MPa)KS1G22 (428.9 MPa)KS1G31 (428.9 MPa)KS1G38 (428.9 MPa)KS1G58 (428.9 MPa)AFGROW 4.0 (324.1 MPa)AFGROW 4.0 (358.5 MPa)AFGROW 4.0 (396.5 MPa)AFGROW 4.0 (428.9 MPa)

Al7050-T7, F18 WRBM


LEFM (AFGROW) crack growth compared to QF of coupon tests

• AL7050-T7351, F/A-18 WRBM Spectrum

• LEFM method required large initial crack size (>0.04mm) to grow cracks – data limitations

• Real initial crack sizes are about 0.01mm

• Shape not correct• Gradient too shallow at

small crack sizes• Gradient too steep at large

crack sizes • Overall not conservative in this

material with this loading0.01

0.1

1

10

-200000.01

0.1

1

10

-20000 -15000 -10000 -5000 0 5000 100000.01

0.1

1

10

-20000 -15000 -10000 -5000 0 5000 10000 15000 20000Time from 0.5mm crack size, T 0.5mm (SFH)

Cra

ck le

ngth

, a (m

m)



Constant Amplitude Loading

GAG Cycle

Crack

Single flight (GAG + 20 cycles)

Deep pockets

Flights

Grains


Crack growth rate models• Frost & Dugdale-based model

• Paris – based model

• General model

• Where for Frost & Dugdale j=1 and k=α, for Paris k=2j

( )krefjaA

tdad σ=

( )ασλ refatdad=

( )mref aCtdad πβσ=

Fast to apply (closed form solution), however usually limited to small cracks

Requires numerical integration, however can handle more complex problems so long as the geometry factor β can be determined


Effective Block Approach • Both models can be used with the QF data for crack growth prediction. Only two constants

required per model.• This “Effective Block Approach” is based on the growth per block of applied loading rather

than constant amplitude loading. • So a ‘reference stress’ is used rather than the applied stress per cycle.• Since most cracks appear to be exponential then the Paris exponent will be about 2, while

analysis of coupon data indicates that the Frost & Dugdale exponent should be about 3.

0.001

0.01

0.1

1

10

0 10 20 30 40 50

KSIF109 450MPaKSIF 126 450MPaKSIF 101 450MPaKSIF103 450MpaKSIF108 450MPaKSIF181 390MPaKSIF207 390MPaKSIF170 390MPaKSIF165 390MPaKSIF182 390MPaKSIF185 330MPaKSIF144 330MPaKSIF123 330MPaKSIF122 330MPaKSIF214 330MPaKSIF112 300MPaKSIF175 300MPaKSIF161 300MPaKSIF201 300MPaKSIF202 300MPa

No. of blocks of applied spectra.0

0.1

0.2

0.3

0.4

0.5

0.6

250 300 350 400 450 500

y = 2.21 x 10-9* x (3.13) R= 0.972

Reference stress (peak stress in spectrum) MPa.


ExtrapolationBoth the Frost & Dugdale-based model and the Paris model allow accurate extrapolation when the empirical constants are derived from the crack growth data taken from testing.

da/dblock = C (Kref.)m

Where Kref = σrefβ√πa

da/dblock = aλ(σref)α

In most cases:m = 2 and α = 3

0.01

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1.00

10.00

-10000 -5000 0 5000 10000 15000 20000Time (hours)

Cra

ck le

ngth

, a (m

m)

Paris m=2 (324.1 MPa)Paris m=2 (358.5 MPa)Paris m=2 (396.5 MPa)Paris m=2 (428.9 MPa)


Prediction under the same load spectrum at different ‘reference’ stresses

• Using the “Effective Block Approach”:• The Frost & Dugdale-based model predicts between stress

levels to give very good results for small cracks with consistent stressing.

• Paris also predicts between stress levels although it is more cumbersome.

• Paris contains the β factor allowing corrections to be made for geometry and stressing changes.


Predictions versus Test ResultsAFGROW 4.0, Harter-T (FT55, 324.1 MPa)

AFGROW 4.0, Harter-T (FT55, 358.5 MPa)



Best fit Paris Law block data

Best fit Paris Law block data, m=2

Best fit Frost-Dugdale Law, α=3 (FT55, 324.1 MPa)




Fastran 3.8, Harter-T (FT55, 324.1 MPa)




AFGROW 4.0, Harter-T, Willenborg (FT55, 324.1 MPa)




1.0E-04

1.0E-03

1.0E-02

1.0E-01

1 10 100

Reference Stress Intensity: Kref (MPa)(m)0.5)

Cra

ck G

row

th R

ate:

da/

dt(m

m/S

FH)

1.0E-05

1.0E-06

1.0E-07

For most of these data m can be set at 2; α = 3


Predict results for one spectrum from another similar spectrum

C and λ are derived from curve fitting for one set of data.

QF coupon data here represents benign and severe RAAF F/A-18 flying.

Predicted severe flying from benign.

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1 10 100Reference Stress Intensity, Kref (MPa(m)0.5)

Cra

ck g

row

th ra

te, d

a/dt

(mm

/SFH

)

Coupon QF data (spec 2)Coupon QF data (spec 1)Best fit Paris m=2 (spec 1)

da/dblock = C (Kref)2

da/dblock = aλ(σref)3


Predicting different spectra cont.

0.0000001

0.00001

0.0001

0.001

0.01

0.1


Cra

ck g

row

th ra

te, d

a/dt

(mm

/SFH

)

Coupon QF data (spec 2)Coupon QF data (spec 1)Best fit Paris m=2 (spec 1)Standard AFGROW (spec 1)Standard AFGROW (spec 2)

1_

2_1_2_

SpectrumAFGROW

SpectrumAFGROWSpectrumCouponSpectrumEstimate C

CCC ×=

( )2aCtdad

ref πβσ=

Use AFGROW with constant amplitude data to predict ‘C’adjustment

Adjust ‘C’

0.000001

Set M = 2


Prediction

Apply C adjustment to best fit Paris with m=2Result are within 20% of coupon data for second spectra

0.0000001

0.00001

0.0001

0.001

0.01

0.1


Cra

ck g

row

th ra

te, d

a/dt

(mm

/SFH

)

Coupon QF data (spec 2)Coupon QF data (spec 1)Best fit Paris m=2 (spec 1)Predicted curve for Paris m=2 (spec 2)

0.000001


Prediction of changes in the CG that can be caused by geometry/load shedding/residual stress conditions

Account for significantly changing β through FE or “back calculate” from QF of similar feature

QF data for the largest crack from two tests shown normalized

0.01

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10

-10000 -5000 0 5000 10000 15000 20000

Cra

ck le

ngth

, a (m

m)

- -

Time from a reference 0.25mm crack size, t0.25mm (spectrum flight hours)

QF data for Hole #8 (Wing test 1)



Calculate the correction factor from the QF dataσβ was calculated from the shape of the QF curves.This correction factor was then applied to the Paris based prediction

0

100

200

300

400

500

0.01 0.1 1 10

Crack length, a (mm)

Stre

ss x

Bet

a di

strib

utio

n, σβ(

a) (M

Pa)

Best σβ fit defined by user (MPa)

QF data for Test 1 hole crack

QF data for Test 2 hole crack


Apply correctionApply β correction to Paris model This gave a good prediction of QF data.An error of +/- 20% in the correction had little influence on the prediction

0.01

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-10000 -5000 0 5000 10000 15000 20000Time from a reference 0.25mm crack size, t0.25mm (spectrum flight hours)

Cra

ck le

ngth

, a (m

m)

QF data for Test I main crackingQF data for Test 2 main crackingPrediction for Test 1 cracking)Prediction for Test 2 cracking

Prediction for Test 2 cracking +20%Prediction for Test 2 cracking -20%


ConclusionsA method has been presented that allows:

• The accurate extrapolation beyond typical crack lengths obtained during testing;

• Predict CG under the same load spectrum at different ‘reference’ stress levels;

• Predict CG of one spectrum from data from another similar spectrum;

• Predict changes in the CG caused by geometry/load shedding/residual stress conditions, and

• Utilization of the most representative data – variable amplitude CG data.


Outcome

• The improved interpretation of fatigue test data has lead to an increased confidence in life predictions.

• This has allowed the RAAF to apply lower safety factors to the crack growth interpretation, and hence increased certified service life for;

• under-tested structure.• structure not correctly loaded with the target spectrum.• cracks in significantly changing stress fields.

Questions?

Fleet fatigue life interpretation from full-scale and coupon fatigue tests - a simplified approach

By

S. Barter, M. McDonald and L. Molent

Abstract When analysing the results of aircraft full-scale fatigue tests to establish fleet life limits, it is best to use as much information from the resultant crack growth (CG) as possible so as to maximise the economical usefulness of the tests. One way to achieve this is to investigate the CG by quantitative fractography (QF) at each location of interest and interpret its applicability to the fleet. The problem often arises that the loading in the location of interest differed from the target loading, either in severity or sequence, causing either little or excessive cracking. Hence additional interpretation is required relevant to the expected fleet loading. With current fracture mechanics based approaches this interpretation can be difficult, inaccurate and very time consuming. To address these difficulties, several additions to the predictive tools currently available have been developed as the result of the analysis of several full-scale fatigue tests on the F/A-18 aircraft. These are aimed at making fleet fatigue life estimation as realistic and fast as practicable. This paper discusses some of the QF results from full-scale and coupon fatigue tests and the additional tools used to establish the lives from the full-scale test cracking when supplemented with relevant QF from coupon test data. The tools include the Frost and Dugdale relationship and the spectrum 'effective block' approach. The former suggests, as a first approximation, that a linear relationship exists between the log of the crack length or depth and the service history (number of cycles). The latter approach treats a block of variable amplitude spectrum loading similarly to the way a single load cycle is treated under constant amplitude loading. Empirical constants for the material under examination, when tested with different spectra, are derived from coupon tests, while geometry effects are extracted from the full-scale test CG data. This then allows rapid and accurate interpretation of full-scale fatigue test CG data for different stress levels and other related spectra. It is shown that the prediction of fatigue life by the present approach is more accurate compared to other crack prediction methods typically used by DSTO and the wider aircraft community. This paper details the methods and discusses the key advantages and disadvantages.

1. Introduction

When analysing the results of aircraft full-scale fatigue tests to establish fleet life limits, it is best to use as much information as possible from any cracking found so as to maximise the economical usefulness of the tests. One way to achieve this is to investigate the growth of these cracks by quantitative fractography1 (QF) at each location of interest and interpret their applicability to the fleet. The problem often arises that the loading in the location of interest was different from the target loading, either in severity or sequence, causing either little or excessive cracking. Hence additional interpretation, aided by supplementary data relevant to the expected fleet loading, is usually required. With current fracture mechanics based approaches this interpretation can be difficult, inaccurate and very time consuming, being usually based on generic, and sometimes difficult to obtain constant amplitude (CA) data interpretation, where although spectrum specific coupon data may be available it has been relegated to being simply used as a calibration tool for the CA based analysis. Clearly, if the lives of the coupons are at all valid then the crack growth (CG) in these coupons is equally as valid, and would serve, if it were possible, to improve the accuracy of any predictions. It is then surprising that the CG of valid coupons is rarely measured. The QF examination of coupon fracture surfaces and full-scale test cracks at DSTO over the past 30 years has indicated that, as a first approximation, the growth of the fastest cracks (no load shedding or residual stresses) will be exponential in nature when a variable amplitude (VA) spectrum loading block is repeated numerous times during a test (ignoring the onset of fast fracture near the end of life). It has also been found that the growth rates for coupons tested at different stress levels with the same spectrum increase at approximately the cube of the stress2 [1]. This is not a new observation, being noted in a study by Frost & Dugdale in 1958 [2] for CA loaded specimens. For example, typical QF CG curves have been plotted for two different aluminium alloys for cracks from shallow radii and open holes in Figure 1, (from [3]), Figure 2 (from [4]) and Figure 3 (from [5]) plotted with log crack depth versus linear life. To indicate that exponential growth is also independent of notch plasticity, data for cracks from open holes in the same material and with the same spectrum as the data in Figure 3 are presented in Figure 4 [6], where the high loads in the spectrum applied did produce notch plasticity at three of the stress levels applied. The straightness of the curves on these presentations strongly suggests exponential growth rates. The relationship between the different stress levels for cracks generated with the same spectrum, for seven different stress levels (four of which are shown in Figure 3) indicated that the growth rates increase at approximately the cube of the stress as shown in Figure 5, where a power curve fit has been made of the exponential slopes of the crack growth curves for 35 cracks with the result that the exponent was, indeed close to 3. This supports the Frost & Dugdale observations when dealing with VA fatigue crack growth. The second of the examples, Figure 2, was taken from a full-scale fatigue test item and shows that cracking is indeed well represented by exponential growth in this case. (Further examples can be 1 Quantitative fractography is the measurement of crack growth on the crack surface after breaking open the crack, rather than monitoring growth using non-destructive surface measurement methods. This can only be carried out after the cracks have been removed from the test article. Since the data gained by analysis of the cracking will be shown here to be of significant importance then it is imperative that any crack that is formed during a test is preserved for latter analysis rather than removed in a destructive manner. 2 For a variable amplitude spectrum the �stress�, or �reference stress,� can be one of several different measures of the spectrum. The easiest measure is the stress produced in the test region of the coupon/article corresponding to the peak tensile load applied in the spectrum. In the plots of coupon tests shown in this paper this is the stress that is noted.

found in [1].) It should also be noted that the exponential nature of this growth down to very small crack sizes was consistent. Crack growth data corresponding to low Kt and high Kt specimens (see Figure 3 and Figure 4) were also measured and the growth was also found to be consistent down to crack depths less than 0.01mm (0.0004 inch) for the load spectra that were applied.

Figure 1. Crack growth in 7475-T7451 F-16 fatigue test coupon fastener holes, from [3]. A schematic

representation of the spectrum applied is shown above the graph. Note its complexity.

0.001

0.01

0.1

1

10

0 10 20 30 40 50 60 70

CB1 Y488 C1CB1 Y488 C2 CB1 Y488 C3 CB1 Y488 C4

No. of Blocks

0.001

0.01

0.1

1

10

0 10 20 30 40 50 60 70

CB1 Y488 C1CB1 Y488 C2 CB1 Y488 C3 CB1 Y488 C4

No. of Blocks

Cra

ck d

epth

(mm

)

Figure 2. Crack growth in an AL7050 plate in the F/A-18 full-scale centre barrel fatigue test loaded with mini-

FALSTAFF, from [4].

0.01

0.1

1

10

100

0 5000 10000 15000 20000 25000 30000

AFLR4 No.1 32KSI Bore AFLR4 No.2 32KSI Bore AFLR4 No.3 32KSI Bore AFLR4 No. 4 32KSI AFLR4 No. 6 32KSIAFLR4 No. 9 32KSIAFLR4 No. 10 32KSI AFLR3 No. 2 32KSI AFLR3 No. 4 32KSI Bore AFLR3 No. 3 32KSI Bore AFMR4 No. 2 34KSI AFMR4 No. 4 34KSI AFMR4 No. 5 34KSI AFMR4 No. 7 34KSI AFMR4 No. 10 34KSI AFMR4 No. 11 34KSI AFMR4 No. 13 34KSI Bore AFMR4 No. 14 34KSI Bore AFMR4 No. 33 34KSI Bore AFMR4 No. 36 34KSI Bore

Simulated Flight Hours

0.001

0.01

0.1

1

10

0 10 20 30 40 50

KSIF109 450MPaKSIF 126 450MPaKSIF 101 450MPaKSIF103 450MpaKSIF108 450MPaKSIF181 390MPaKSIF207 390MPaKSIF170 390MPaKSIF165 390MPaKSIF182 390MPaKSIF185 330MPaKSIF144 330MPaKSIF123 330MPaKSIF122 330MPaKSIF214 330MPaKSIF112 300MPaKSIF175 300MPaKSIF161 300MPaKSIF201 300MPaKSIF202 300MPa

No. of blocks of applied spectra.

Figure 3 Example of crack growth data from QF of coupons tested under an F/A-18 RAAF wing root bending moment spectrum. (Experimental results are from [5] and are for Al7050-T7451 coupon specimens, with a low notch-stress factor (Kt about 1), subject to the F/A-18 spectrum applied at four different scale levels.)

0.001

0.01

0.1

1

10

100

0 20 40 60 80 100 120

KK1H 346 250MPaKK1H 356 250MPaKK1H 374 250MPaKK1H 334 250MPaKK1H 373 250MPaKK1H 325 225MPaKK1H 377 225MPaKK1H 337 225MPaKK1H 370 225MPaKK1H 322 225MPaKK1H 328 200MPaKK1H 338 200MPaKK1H 340 200MPaKK1H 342 200MPaKK1H 385 200MPaKK1H 348 155MPaKK1H 193 155MPaKK1H 339 155MPaKK1H 343 155MPaKK1H 353 155MPa

No. of Blocks

Figure 4 Shown are examples of crack growth data from QF of coupons tested under an F/A-18 RAAF wing root bending moment spectrum). (Experimental results are from [6] and are for Al7050-T7451 coupon specimens, with a high notch-stress factor (Kt - about 3), subject to the F/A-18 spectrum applied at four different scale levels, with notch plasticity at the high stress levels).

0

0.1

0.2

0.3

0.4

0.5

0.6

250 300 350 400 450 500

y = 2.21 x 10-9 * x (̂3.13) R= 0.972

Reference stress (peak stress in spectrum) MPa. Figure 5 Shown are the crack growth slopes extracted from the crack growth data shown in Figure 3 along with

data for thee other stress levels using exponential cure fitting. (Experimental results are from [5].) Note the value of 2.21 x10-9 is in mm/block.

This paper presents a method for using and interpreting coupon and full-scale test QF CG results where available, to achieve a better life predication result based on the observed nature of CG in the materials of interest when loaded with spectra representative of a fighter aircraft. As an example, data from a program used to aid in the lifing of the F/A-18 aircraft in Royal Australian Air Force (RAAF) service is used. 1.1 Lifing difficult regions of the F/A-18

Although much full-scale fatigue testing has been carried out on the F/A-18 aircraft, there were several major components that were not adequately covered by the results of these tests. If exponential growth is found to be applicable, as it has been to many parts of the F/A-18 aircraft [11], then a simple visualisation of the airworthiness regulation, DEFSTAN 00-9703 [7], under which the RAAF F/A-18 aircraft are certified, can be represented in terms of CG as an exponentially growing crack, starting from a typical (not extreme) crack like discontinuity in an aircraft component of 0.01mm deep (typical for high strength aluminium alloys) as shown in [11]. The following assumptions related to cracks in critical structure in agile aircraft were used to produce Figure 6:

(1) Critical structure has been well designed (optimised) such that, at the end of full-scale fatigue testing, the cracked structure just meets the DEFSTAN�s residual strength

3 DEFSTAN 00-970 dictates that for an aircraft managed by the safe-life method, full-scale fatigue testing needs to be carried out for 5 or more times the expected service life of the airframe (a scatter factor of 31/3rd for fatigue scatter and a further factor of 1.5 for those structural elements not monitored in service).

requirement (and the USAF4 structural impairment criterion [8] the USN crack initiation criterion5 [8], which have been included for comparison in Figure 6);

(2) Cracks grow exponentially with time (as indicated above). The small fraction of life involved in fast fracture or tearing can be ignored;

(3) Critical cracks commence growing shortly after the aircraft is introduced into service (as can be seen in the examples shown in Figure 2-4;

(4) Typical (mean) initial flaws for fighter aircraft material (aluminium alloys) are approximately equivalent to a 0.01 mm deep crack [1][11] and Figure 1-4; and

(5) The critical crack depth is typically 10 mm [1][11] for a highly stressed area.

The DEFSTAN also includes additional factors on the safe life that are to be used to multiply the typical 3⅓ safety factor6 applied to the test result, for among other things, to account for such issues as structure that will not be monitored (for loads) in service, or structure certified by a safe S-N analysis alone (without testing). The required CG data for each of these, when used in RAAF certification, are also included in Figure 6. This Figure shows that at the end of a typical DEFSTAN fatigue test (it is usually not practical to test full-scale structure much beyond about 3 service lives, for many reasons) very small cracks in critical parts of an aircraft�s structure will exist. These cracks may be critical in-service due to; under testing, larger initiating discontinuities in service aircraft, more severe usage etc. This makes it desirable to be able to project forward the crack growth of these flaws to failure to establish a �pesudo� test life.7 So to clear such structure to the DEFSTAN an analysis that accurately predicts final crack life is needed. It is also required that should it be found that the spectrum applied was not correct in the area of interest, or the service flying has changed in some fashion, then the prediction of crack lives under these different conditions is needed without the excessive safety factor that is required for analysis alone (assumed to be 10 in Figure 6).

4 The USAF method employs durability and damage tolerance analysis (underpinned by a durability full-scale fatigue test). The USAF crack growth analysis requires that a typical manufacturing flaw (assumed to be 0.125 mm deep) shall not grow to functional impairment (assumed to be a crack approximately 10 mm deep here) in two times the design service life. This requirement is supported by two lifetimes of durability testing. USAF damage tolerance analysis is required to show, for example, that a 1.27 mm crack at a hole will survive for two service lives (assuming safety is also not maintained by inspection). The CG in this analysis should be supported by CG data from testing. 5 The USN requires that crack initiation (defined as a 0.254 mm deep flaw) does not develop during two lifetimes of durability testing. 6 The safe S-N method is prescribed by DEFSTAN for the calculation of a test life factor. This typically leads to a factor of approximately 3⅓ for a typical fighter aircraft spectrum at relatively high stresses. For components loaded with less severe spectra or containing many low loads, or are dominated by aerodynamic buffet, higher factors can arise. For example, it was found that for typical F/A-18 aft fuselage and empennage components factors of approximately 5 were obtained. As these components are not subject to in-service loads monitoring an additional factor of 1.5 on life was required, leading to the capped factor of 7.5 used in Figure 6 [9][10]. 7 For instance, DEFSTAN defines the test life as that time just before a detected crack becomes critical under the application of 1.2x Design Limit Load.

0.01

0.1

1

10

0 2 4 6 8 10

Typical DEFSTAN test - Safe lifeTypical DEFSTAN test + no monitoring in serviceTypical DEFSTAN test + buffet factorTypical DEFSTAN Safe S-N analysis aloneUSAF crack growth analysisUSAF durability testDamage tolerance analysisUSN durability test - Safe life

Design service life

Typical maximum test life

Figure 6 A comparison of various aircraft fatigue design and test requirements, assuming a final crack

length of 10mm and a natural typical initial crack length of 0.01mm.

Presently, many difficulties exist with the accuracy of current CG prediction methods necessitating the investigation of alternatives. Of these, an empirical methodology based on observed CG behaviour as measured by QF for VA loaded structures (fighter type spectra) is proposed here for application to critical F/A-18 outer wing and empennage components, which are both affected by buffet loading. This methodology is based on the use of empirical tools to relate CG results from coupons tested under a range of spectra, to the spectra applicable to the difficult to clear RAAF F/A-18 components. The proposed method combines the results of detailed QF from full-scale F/A-18 fatigue tests with coupon test results that have also had their CG measured by QF. For the CG prediction model to be useful, four capabilities were needed:

• The ability to accurately extrapolate beyond typical crack lengths obtained during testing; • The prediction of cracking under the same load spectrum at different �reference� stresses; • The prediction of changes in the CG that can be caused by geometry/load

shedding/residual stress conditions; and • A robust tool for comparing different spectra at similar loading severity.

The simple empirical fatigue CG tools presented here have shown considerable promise when compared to actual CG data generated by QF, whereas the predictions based on CA fatigue data have instilled a lack of confidence. Comparison between QF results and the predictions of existing CG �tools� which rely on CA data, i.e. software programs such as AFGROW and FASTRAN�s, has shown that the results of these analyses have significant error when applied to VA loaded structures such as those found in the F/A-18. They can significantly under estimate CG rates when the stress intensity, K, at the crack tip is small and/or the crack length is short (resulting in over estimation of life and reduced safety margins, (see Figure 78,) where crack sizes are small). In fact, for very small crack sizes they can predict no CG at all (this is dependent on

8 Data for thick section plate Al7050-T7451 coupon specimens loaded with a fighter wing root bending moment spectrum representative of RAAF flying. Al7050-T7451 is a material that is used extensively in the critical structure of the F/A-18 aircraft.

the da/dN data used in these tools,9 not necessarily the tools themselves). Conversely, when K is large and/or cracks are long, they tend to over estimate CG rates (resulting in under estimation of life, and thus a potential for capability loss or overly short inspection intervals should a safety-by-inspection program be needed, see Figure 7, where the crack sizes are large). The errors appear to be due to several factors:

• The difficulty in translating CA CG rates into CG rates for VA spectra; • The methods used to generate the CA data (for example [12]); • The use of long-crack material data for short-crack problems; • The use of material data derived from experiments using specimens of different thickness,

hardness, grain structure, etc; • The use of short crack data taken from surface measurements [13] instead of crack depth; • Orientation of the crack used to generate the da/dN data compared to the component

crack; and • The use of K for very small cracks where the size of the plastic zone is large compared to

the crack length.

0.01

0.1

1

10

-20000 -15000 -10000 -5000 0 5000 10000 15000 20000Time from 0.5 mm crack size, T0.5 mm(SFH)

Cra

ck le

ngth

, a (m

m)


0.01

0.1

1

10

-20000 -15000 -10000 -5000 0 5000 100000.01

0.1

1

10

-20000 -15000 -10000 -5000 0 5000 10000 15000 20000Time from 0.5 mm crack size, T0.5 mm(SFH)

Cra

ck le

ngth

, a (m

m)


Figure 7 Typical comparison of the results from the crack growth prediction tool AFGROW and experimental

crack growth data (from [14]), where all data are pegged to a 0.5 mm crack size for comparison purposes (spectrum applied at four different stress levels).

The differences between predictions made by the tools, and measurements from VA loaded coupon tests, can be plus or minus an order of magnitude from the mean coupon life, hence the

9 The inclusion of good short-crack growth data would improve this situation, although such reliable data are still difficult to obtain.

safety factor used by the RAAF/DSTO for fatigue clearance by analysis (approximately 10), which is much larger than typical coupon testing fatigue scatter (usually between 0.5-2 of the mean life). Minimisation of the risk of an unnecessarily short life prediction can be made through reducing the erroneous �scatter� introduced by the life prediction methodology. This can be achieved by reducing the unknowns in: the loads applied to the test article, material factors, service history tracking, build-quality, test versus service environment, or importantly by using a more reliable fatigue lifing model. Here an empirical relationship that serves as the fatigue life CG model, which has been shown to agree well with observations of CG data from coupon fatigue tests, is used to increase the reliability of a components service life prediction.

2. Methodology

2.1 Introduction

The F/A-18 fatigue testing involves discrete blocks of loading spectra representative of about 250 hours of service flying applied repeatedly over the life of a full-scale fatigue test, as is typical of many full-scale fighter aircraft fatigue tests. This allows QF to track CG on crack surfaces by measuring the pattern left as a result of this repeat in the block loading.10 With the results of such CG curves at hand, an effective block approach can be considered whereby the CG rates are quantified and modelled as if each block of loads was a single cycle of CA loading. Naturally, data from the full-scale test article, but in its absence data from a well designed coupon test program11, can be used to extract the empirical constants for use in the CG model. Fortunately, there exists good quality CG data generated by QF for several F/A-18 wing root bending moment (WRBM) spectra and empennage spectra [11], which include buffet; making it possible to predict CG rates for any RAAF equivalent spectrum. 2.2 Summary of the Effective Block Approach

The effective block approach requires several inputs and conditions to be addressed:

• Data from block spectra must be repeated a significant number of times over the life of the structure so that a CG curve produced has sufficient fidelity (for F/A-18, typically > 30 blocks).

• The block of the VA loading is treated in a similar way to a cycle in a CA loading. • VA loaded coupons are required to measure CG12 rates (da/dt) for different reference

stresses. This is similar to the way that CA loaded coupons are used to measure CG as crack length per load cycle when producing typical da/dN data.

10 A good deal of effort may be needed to make this possible with some spectra, although with skilled practitioners QF will usually be possible on most fractures from full-scale testing when flight-by-flight loading is used. The QF process followed at DSTO is reported in [21]. 11 The structural detail under investigation, including manufactured surface condition, material type, material orientation and loading, to name a few factors, should be considered, although in many cases the coupons can be surprisingly simple in design. 12 CG measurement of coupons can be carried out by either in-situ (test machine) measurement or by QF after failure. In-situ measurement has been found to be mostly impractical and/or inaccurate for very small cracks.

• A suitable CG model (e.g. adaptation of Paris (or �Paris-like�) [15], or a simpler model such as Frost & Dugdale) has to be selected that is readily applicable to the effective block approach.

• Determination of empirical constants for the chosen models derived from the VA QF data. • Use of the chosen models, along with their constants, to interpret or predict CG for the

different conditions present in the airframe.

Also, the range of applicability of the approach needs to be well understood, particularly its robustness, including, for example, the ability to account for stress scale levels, various geometry effects, and plane stress/strain effects. Limitations may include problems associated with crack acceleration/retardation due to one-off underloads or overloads, etc. 2.3 Empirical crack growth models for block spectra analysis

The general form of the empirical CG model considered here is:

( )kref

jaAtdad σ= (1)

where a is the crack length or depth, t is the crack life (number of spectrum flight hours here), σref is a semi-arbitrary reference stress of the block spectrum as applied to the coupons from which the CG was derived (e.g. peak far field stress), and A, j and k are empirical constants derived from experimental data. The value of the exponents in Eqn (1) can vary depending on various assumptions about the CG mechanism. In evaluating the best approach for the F/A-18 lifing calculations, two variations are investigated here: the Frost & Dugdale model [2] and the Paris based model [15]. Frost & Dugdale considered that for small crack lengths (compared to the net-section size) the crack length exponent j should be equal to 1, which results in an exponential CG curve (a straight line when the crack depth a is plotted on a log scale against time t on a linear scale, see [1]). The second, based on Paris, considers from linear elastic fracture mechanics (LEFM) theory, that the CG should be a function of the stress intensity factor K, which is proportional to σ√a (i.e. j=2k). This paper does not attempt to discuss the fundamental reasoning behind the derivation of the two forms, rather, each are treated as empirical equations where the respective constants are simply fitted to VA experimental data. 2.3.1 Frost & Dugdale-based model (Log-Linear)

This simple model relates the CG rate to the crack length and a spectrum reference stress level. The Frost & Dugdale CG equation can be shown in power form (see Eqn (2a)) or Log-Linear form (see Eqn (2b)).

log(a)-linear(t) form: [ ] ( )ασλ reftd

ad=

)ln( (2a)

or, da/dt form: ( )ασλ refatdad

= (2b)

where λ and α are empirical constants derived from experimental data. Here j=1 and k=α when compared to the general form of the CG model given in Eqn (1). Frost & Dugdale showed by CA

experiments [1] that the stress exponent α is typically around 3; hence the Frost & Dugdale model is sometimes also referred to as the �stress-cubed� law. However, the stress exponent has been left as a variable here so that its value can be confirmed for the VA loading considered here. Although this model was originally developed for constant amplitude CG, it has been shown [1] to apply to repeating blocks of VA loading since it contains empirical constants that can be drawn from VA coupon test data. For instance, it can be seen in Figure 5 that for the VA data shown in Figure 3 α is about 3. The CG rate equations can be re-arranged into a �y=Mx+B’ form as for determining the empirical constants by linear regression as follows:

From Eqn (2a) [ ] ( ) ( )reftd

ad σαλ lnln)ln(ln +=

(3a)

or, from Eqn (2b) ( ) ( )refatdad σαλ lnln1ln +=

(3b)

where the terms ln(λ) and α correspond to the intercept and the slope respectively. The main advantage of this model is its computational efficiency, since the integration of Eqn (2) results in the following simple closed-form solution for the crack size as a function of time:

[ ]taa refoασλ )(exp= (4)

A limitation of this model is; that it does not inherently determine the final crack size, which must be input just like the initial crack size (a0). This must be determined separately by conventional fracture mechanics. When a0 is very much smaller than af (critical) (which is usually the case for ductile alloys) the final life is not very sensitive to the critical crack size (af (critical)). Here the choice of a0 becomes of major importance. Also, the results may not be applicable if applied to large crack lengths (compared to the net-section size), different crack geometries, different component geometries, or; component loading/boundary conditions, due to the absence of a geometry and loading/boundary condition (β) factor in the CG model. However, it is considered that, for a substantial range of practical problems, a large proportion of the total fatigue life can occur within these limitations [1]. 2.3.2 Paris-based model (LEFM)

This model relates the CG rate to the stress intensity at the crack tip, as derived empirically from CG data. Similar to the Frost & Dugdale model, it is expected to apply to repeating blocks of fighter-type spectra. The model predicts a straight CG rate curve when the rate (da/dt) is plotted on a log scale against the reference stress intensity factor (Kref) also on a log scale between a threshold region and a terminal region of growth. The power form of the Paris-based CG rate equation is:

( )mrefKC

tdad

= (5)

where C and m are empirical constants derived from experimental data, and Kref is a semi-arbitrary reference stress intensity factor for the spectrum under consideration (for example the peak K), rather than ∆K which is usually used for VA CG estimation from CA data. Eqn. (5) can be re-arranged into a �y = Mx+B’ form for determining the empirical constants by linear regression as follows:

( ) ( )refKmCtdad lnlnln +=

(6)

where ln(C) and m correspond to the intercept and the slope respectively. The equation for the reference stress intensity factor is:

aK refref πβσ= (7) where σref is an arbitrary reference stress from the block spectrum (e.g. peak stress), and β is a function accounting for changing stress intensity due to the changing crack front shape, component geometry, and remote loading constraint conditions. This is usually expressed as a function of crack length, i.e. β = β(a) and may be complex. Integrating the Paris-type model can yield a closed-form solution, though it is usually numerically integrated due to the complex forms of β for real structures. For the special case where β is constant, integrating Eqn (5) and (7) yields the following closed-form solutions for crack size as a function of time:

For constant β and m = 2 only: [ ]tCaa refo2)(exp βσπ= (8a)

For constant β and m ≠ 2: ( ) ( )

−−

−+= 22

11

21 )1(

mm m

refm

o Ctaa πβσ (8b)

The main advantage of this model is that it contains the β function, which allows analysis of cracks that are long compared to the component or coupon geometry. This function also allows correlation between different crack shapes and component geometries, since β solutions for various problems have been published in numerous handbooks (for example [16][17][18][19]). It also allows analysis of more complicated scenarios, e.g. those involving load shedding, so long as a β function can be determined to account for such effects. The Paris-type model can be expected to have similar limitations to LEFM-based CA models (e.g. restriction to small-scale yielding, i.e. the size of the plastic zone at the crack tip should be small compared to the crack and component geometry dimensions). Furthermore, since the stress σref and size a terms are lumped together within Eqn (5), which is subsequently raised to a single power m, then it is not possible to model, or even �pseudo model�, separate stress and crack size relationships. For example, it is not possible to model complications due to the effect of stress such as: mean stress effects (i.e. similar to the R-ratio effect in CA CG); plane stress-strain effects; or any other effect that may cause deviation away from the j=2k relationship from Eqn (1) that Paris imposes. Rather, an �effective average� of any such effects would be simply built into the derived CG rate constants C and m, which will be dependent on the particular conditions of the coupon test. Clearly a decoupled a term would improve the model [1].

2.3.3 Comparison of the Paris and Frost & Dugdale type models

It is important to recognise that the two models are of different form, hence the coefficients and exponents are not interchangeable. The Frost & Dugdale model always produces a straight CG curve, for all values of α, when a is plotted against t on a log and linear scale respectively. Conversely, the Paris model only produces a straight a versus t (log-linear) line when m=2 and β is constant, otherwise the CG curve will have a more upward (accelerating) trend when m>2 or a more downward (decelerating) trend when m<2. Thus the Paris-type model yields an identical CG curve to the Frost & Dugdale model only when m = α = 2 and β = π

-½ (compare Eqn (4) and (8a)). This is

pertinent for the F/A-18 lifing work since CG analyses of 7050-T7451 aluminium alloy coupon cracks appear to result in values of m close to 2 for several VA spectra [20]. These included the most severe and benign WRBM usage in the RAAF [14]. Although the Paris m exponent is about 2 for these spectra when applied to F/A-18 material, it does not imply that the α exponent in Frost & Dugdale should also be near 2. The Paris exponent m affects both the stress and crack length terms, thus changes in m can affect not only the slope of the CG curve, but also its shape. Conversely, the exponent α in Frost & Dugdale does not affect the shape of the CG curve at all since it does not affect the crack length term; in fact the Frost & Dugdale based model can produce identical CG curves for both α = 2 and 3, simply by adjusting the coefficient λ.

For the Frost & Dugdale based model, the independence between the exponent on the crack length term (i.e. 1) and the exponent on the stress term (i.e. α) gives it the ability to model CG behaviour that does not comply with the Paris constraint of j=2k imposed upon Eqn (1). For example, the F/A-18 coupon QF data displayed a mean stress effect (similar to the R-ratio effect for CA CG) whereby the CG rates (per Kref) increased slightly as the load spectra were scaled up. The WRBM load spectra typically have an average value of stress that is non-zero, therefore allowing a mean stress effect to occur when scaling the normalised loads. The Frost & Dugdale relationship is able to account for this behaviour and this is most likely the reason that the values for the stress exponent α tend to be closer to 3 rather than 2. 2.4 Using the Effective Block Approach to interpret coupon QF data

The process proposed here is as follows:

• For all coupons, group a versus t data by spectrum. Figure 3 shows an example of QF data for a single typical spectrum, from [5].

• Convert the CG data (a versus t) into CG rate data (for Paris use da/dt versus Kref ; for Frost & Dugdale use dln(a)/dt versus σref. This can be achieved in several ways. Here, for each individual coupon, the da/dt data for Paris, or dln(a)/dt data for Frost & Dugdale, was calculated by taking the slope of the line of best fit (linear regression) of each [a, t] data point and its two immediate neighbours (the slope averaged over the three points to smooth out slight measurement variations in the QF data).

• For the Frost & Dugdale based model, plot all of the dln(a)/dt against the relevant reference spectrum stress scale level, σref, for the coupons. An example of the resulting CG rate data calculated using the Frost & Dugdale model is given in Figure 8. Data are from [14]. The linear regression is then carried out to determine the constants. For this example

data the best fit linear regression constants were λ=4.96×10-15 mm/SFH, and α=3.13, which is plotted on the graph.

• For the Paris based model, plot all of the da/dt data against the relevant reference spectrum stress intensity level Kref that was applicable at each data point. It is necessary to determine a β solution for the coupons, either from an available textbook standard solution or finite element (FE) analysis. For the F/A-18 coupon specimens shown here, a standard corner crack solution was used, as shown in Figure 9. The CG rates data calculated using the Paris model are given in Figure 10 for the same set of data as above. The linear regression is then carried out to determine the constants: C =1.32×10-9 m/hour, and m=2.14 for these data.

• For the F/A-18 spectra, the α and m constants were found to be fairly constant between six spectra analysed, which included spectra other than WRBM spectra [5][14][21][22], i.e. spectra containing large amounts of buffet, applied to materials other than high strength aluminium alloys [23], hence these have been fixed at a nominal α=3 and m=2 respectively. The fixing of these constants result in different best fit values for the other coefficient constants, e.g. when α=3 then λ=7.47×10-15 m/hour, similarly when m=2 then C=1.78×10-9 m/hour13.

Both model fits are plotted on a single LEFM type graph (da/dt vs Kref) in Figure 11. It can be seen in this Figure that both CG models provide a good fit of the QF CG data. The Frost & Dugdale CG model appears to capture a �mean stress� type effect (similar to the �R-ratio� effect observed in CA CG data). Conversely, the Paris based model does not capture this effect; rather it represents an average fit through the whole data set. It can also be seen that as the Kref magnitude becomes greater (particularly when Kref > 20MPa√m), the Frost & Dugdale and Paris based CG models deviate more significantly from each other, which is expected to be due to the crack length approaching the thickness of the coupon specimen. This is where the Paris based model�s uses of the β solution has a significant effect.

Reference stress, ref (MPa)

600

Reference stress, σ (MPa)

2001.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-08

1.0E-07

1.0E-06

1.0E-05

Cra

ck g

row

th ra

te, d

( ln(a

))/d

t( ln

(m)/S

FH)

Cra

ck g

row

th ra

te, d

( ln(a

))/d

t( ln

(m)/S

FH)

Reference stress, ref (MPa)

600

Reference stress, σ (MPa)

2001.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-08

1.0E-07

1.0E-06

1.0E-05

Cra

ck g

row

th ra

te, d

( ln(a

))/d

t( ln

(m)/S

FH)

Cra

ck g

row

th ra

te, d

( ln(a

))/d

t( ln

(m)/S

FH)

Cra

ck g

row

th ra

te, d

( ln(a

))/d

t( ln

(m)/S

FH)

Cra

ck g

row

th ra

te, d

( ln(a

))/d

t( ln

(m)/S

FH)

KS1G9 (324.1 MPa)KS1G3 (324.1 MPa)KS1G36 (324.1 MPa)KS1G54 (324.1 MPa)KS1G66 (324.1 MPa)KS1G48 (324.1 MPa)KS1G18 (358.5 MPa)KS1G46 (358.5 MPa)KS1G41 (358.5 MPa)KS1G32 (358.5 MPa)KS1G69 (358.5 MPa)KD1R13 (396.5 MPa)KD1R23 (396.5 MPa)KD1E10 (396.5 MPa)KD1R12 (396.5 MPa)KD1P14 (396.5 MPa)KD1P24 (396.5 MPa)KS1G29 (396.5 MPa)KS1G14 (428.9 MPa)KS1G22 (428.9 MPa)KS1G31 (428.9 MPa)KS1G38 (428.9 MPa)KS1G58 (428.9 MPa)




Figure 8 Example of crack growth rate data calculated for the Frost & Dugdale based model using the QF data

given in Figure 7 and Eqn (3a).

• 13 Reminder: the CG rate constants for the Paris and Frost & Dugdale based models are not interchangeable. Furthermore, the coefficients λ and C are sensitive, and hence specific, to the magnitude of the exponents α and m respectively.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 1 2 3 4 5 6 7 8 9 10Crack length, a (mm)

Bet

a, ß

Beta solution used for F/A-18low Kt coupons (both Pell andBarter programs)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 1 2 3 4 5 6 7 8 9 10Crack length, a (mm)

Bet

a, ß

Beta solution used for F/A-18low Kt coupons (both Pell andBarter programs)

Figure 9 Beta (geometry and constraint) solution used to interpret the F/A-18 coupon crack growth data, when

using the Paris based CG model.

1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1 10 100Reference Stress Intensity, Kref (MPa)(m)0.5

Cra

ck g

row

th ra

te, d

a/dt

(m/S

FH)


1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1 10 100Reference Stress Intensity, Kref (MPa)(m)0.5

Cra

ck g

row

th ra

te, d

a/dt

(m/S

FH)


Figure 10 Example of crack growth rate data calculated for the Paris based model using the QF data given in Figure 7 and Eqn (6).

1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1 10 100Reference stress intensity, K ref

Cra

ck g

row

th ra

te, d

a/dt

(m/S

FH)

KS1G9 (324.1 MPa)

KS1G3 (324.1 MPa)

KS1G36 (324.1 MPa)

KS1G54 (324.1 MPa)

KS1G66 (324.1 MPa)

KS1G48 (324.1 MPa)

KS1G18 (358.5 MPa)

KS1G46 (358.5 MPa)

KS1G41 (358.5 MPa)

KS1G32 (358.5 MPa)

KS1G69 (358.5 MPa)

KD1R13 (396.5 MPa)

KD1R23 (396.5 MPa)

KD1E10 (396.5 MPa)

KD1R12 (396.5 MPa)

KD1P14 (396.5 MPa)

KD1P24 (396.5 MPa)

KS1G29 (396.5 MPa)

KS1G14 (428.9 MPa)

KS1G22 (428.9 MPa)

KS1G31 (428.9 MPa)

KS1G38 (428.9 MPa)

KS1G58 (428.9 MPa)

Best fit Paris Law, m=2

Best fit Frost & Dugdale Law, α=3 (324.1 MPa)

Best fit Frost & Dugdale Law, α=3 (396.5 MPa)Best fit Frost & Dugdale Law, α=3 (358.5 MPa)


1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1 10 100Reference stress intensity, K ref

Cra

ck g

row

th ra

te, d

a/dt

(m/S

FH)

KS1G9 (324.1 MPa)

KS1G3 (324.1 MPa)

KS1G36 (324.1 MPa)

KS1G54 (324.1 MPa)

KS1G66 (324.1 MPa)

KS1G48 (324.1 MPa)

KS1G18 (358.5 MPa)

KS1G46 (358.5 MPa)

KS1G41 (358.5 MPa)

KS1G32 (358.5 MPa)

KS1G69 (358.5 MPa)

KD1R13 (396.5 MPa)

KD1R23 (396.5 MPa)

KD1E10 (396.5 MPa)

KD1R12 (396.5 MPa)

KD1P14 (396.5 MPa)

KD1P24 (396.5 MPa)

KS1G29 (396.5 MPa)

KS1G14 (428.9 MPa)

KS1G22 (428.9 MPa)

KS1G31 (428.9 MPa)

KS1G38 (428.9 MPa)

KS1G58 (428.9 MPa)

Best fit Paris Law, m=2


Best fit Frost & Dugdale Law, α=3 (396.5 MPa)Best fit Frost & Dugdale Law, α=3 (358.5 MPa)


Figure 11 Comparison of the Frost & Dugdale (α = 3, λ = 7.47×10-15 m/SFH) and Paris (m = 2, C = 1.78×10-9

m/SFH) based CG model fits of the QF data given in Figure 3, plotted on an LEFM-type graph.

2.5 Method for predicting relative spectrum CG rates

Different CG rates (for the same σref) for the different F/A-18 spectra are generally observable from the coupon QF data that have been analysed so far. This observation is quantified by the best fit empirical constants for each spectrum when using CG models based on either Paris or Frost & Dugdale. These results have shown that different spectra tend to have a significant effect on the empirical coefficient constants in the CG models (C in Paris, or λ in Frost & Dugdale). However, they had little effect on the empirical exponent constants (m in Paris, or α in Frost & Dugdale). Hence, to assess relative CG rates between spectra, the coefficient ratios between spectra (i.e. ratios of C and λ) can be used. A tool or method is required to predict the relative differences in C and λ caused by the different spectra. The tool should account for the various characteristics of VA block spectra, namely: stress magnitudes, the number of occurrences, and stress sequence order. The relative spectrum analysis tools that have been trialled as part of the development of this method were: AFGROW [24], FASTRAN [25], CI89 [26] and numerous other �single quantity� spectra characteristics (e.g. average stress magnitude). For application to the effective block approach, it is supposed that there exists a linear scalability between the CG rate coefficients derived from different sources. That is, the ratio between the coefficients derived from coupon tests is equal to the same ratio derived from some analysis tool, resulting in the following relative-spectra relationship for the Frost & Dugdale and Paris based models respectively:

1_

2_

1_

2_

spectrumTool

spectrumTool

spectrumCoupon

spectrumCoupon

λλ

λλ

= (9a)

or, 1_

2_

1_

2_

spectrumTool

spectrumTool

spectrumCoupon

spectrumCoupon

CC

CC

= (9b)

Using these ratios, it would be a simple task to calculate new constants for another similar spectrum if a suitable tool is used to predict the new constants. For example, using AFGROW as the relative spectrum analysis tool (since it is relatively easy to use), a standard AFGROW CG of each of two aircraft spectra, where both the constants are known for one spectra was carried out. In this example two WRBM spectra derived from two RAAF aircraft representing the more severe (designated here as A1) and the more benign (designated here as A2) flying in RAAF service were analysed. The AFGROW analysis resulted in the CG data given in Figure 12 for the two spectra. It is noteworthy that the growth shape was not typical of the growth observed in the actual coupons (or full-scale fatigue test failures for that matter). The data can then be used in the effective block approach using both the Frost & Dugdale and Paris based CG models, and relative spectra assessments made.

1.0E-02

1.0E-01

1.0E+00

1.0E+01

0 5000 10000 15000 20000 25000 30000 35000

Time, t (SFH)

Crack length, a

(mm)

Standard AFGROW prediction (Benign Aircraft A1)Standard AFGROW prediction (Severe Aircraft A2)

Figure 12 Standard AFGROW predictions for crack growth in the Al7050 material subject to the A1 and A2

WRBM load spectra (for a reference stress of 396.5 MPa, and a pre-crack size of 0.04 mm).

The Frost & Dugdale model on the standard AFGROW CA CG data resulted in coefficients of λAFGROW_A1 = 1.533×10-15 and λAFGROW_A2 = 4.168×10-15 (m/SFH) when the stress exponent was fixed at α = 3. Now consider that coupon tests using the A1 spectrum were performed, resulting in a λ coefficient of CCoupon_A1 = 3.838×10-15 (actual result from a coupon test program [14]). Then the λ prediction for the A2 spectrum is given by: λPrediction_A2 = λCoupon_A1 × (λAFGROW_A2/λAFGROW_A1) = 3.838×10-15 × (4.168×10-15/1.533×10-15) = 10.43×10-15 (m/SFH) The actual coefficient for the A2 spectrum, as derived from coupon tests was 11.46×10-15 (m/SFH), showing that the error in the CG rate prediction for this case was about 9%, which is considered small for such an analysis. Similarly, the Paris based model, using the standard AFGROW CG data resulted in coefficients of CAFGROW_A1 = 0.588×10-10 and CAFGROW_A2 = 1.362×10-10. (Here an m value of 3.28 was used, which was an average best fit of the standard AFGROW CG data for this material, and is typical of long crack data for this material.) Now consider that coupon tests for the A1 spectrum were performed and resulted in a coefficient of CCoupon_A1 = 8.733×10-10 and m = 2 (actual result from coupon testing [14]). Then the prediction for the A2 spectrum is given by:

CPrediction_A2 = CCoupon_A1 × (CAFGROW_A2/CAFGROW_A1) = 8.733×10-10 × (1.362×10-10/0.588×10-10) = 20.2×10-10 (m/SFH) Assuming that the m value for the predicted spectrum (A2) will be the same as that for the coupon tested spectrum (A1), i.e. m = 2. The actual coefficient for the A2 spectrum derived from coupon tests was 25.6×10-10 m/SFH (m = 2), showing that the error in the CG rate prediction for this case was about 20%, which is also considered small (cf. generally, analytical life estimation errors can be an order of magnitude). The standard AFGROW results, the coupon results and the prediction of A2 from A1 are all plotted on a Paris type representation in Figure 13. Note that in this Figure the standard AFGROW results using the CA data give poor predictions of the measured crack growth for the coupons at the higher and lower rates leading to the differences in the crack growth curves noted in Figure 7

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1 10 100Reference Stress Intensity, Kref (MPa)(m) 0.5

Cra

ck g

row

th ra

te,

da/d

t(m

m/S

FH)

Coupon QF data (spectrumA1)Coupon QF data (spectrum A2)Best fit Paris -based model (spectrum A1)Standard AFGROW prediction (spectrum A1)Standard AFGROW prediction (spectrum A2)Predicted curve for spectrum A2

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1 10 100Reference Stress Intensity, Kref (MPa)(m) 0.5

Cra

ck g

row

th ra

te,

da/d

t(m

m/S

FH)

Coupon QF data (spectrumA1)Coupon QF data (spectrum A2)Best fit Paris -based model (spectrum A1)Standard AFGROW prediction (spectrum A1)Standard AFGROW prediction (spectrum A2)Predicted curve for spectrum A2

Figure 13 Paris based CG rate plot of the standard AFGROW CG data shown in, for determination of the

best fit coefficients C for the A1 and A2 WRBM load spectra (m = 3.28. This relative difference in the AFGROW predictions were than applied to the Paris prediction (using m = 2) to predict A2 from A1. The coupon data for both A1 and A2 is also shown.

Overall, this example shows that it is possible to predict the lives of structure tested under different related spectra from known spectra data with reasonable accuracy. This can be accomplished with readily available CG programs such as the standard AFGROW program, while they may not be considered a good predictor of the absolute CG curve using CA data; they are shown here to be reasonable predictors of relative severity. 2.6 Effective Block Approach: Non exponential growth -- F/A-18 wing example

Although for most cracks the Frost & Dugdale model is usually easier to use, it is not always appropriate. When the cracking does not appear to follow a exponential growth model since it has been affected by some complex load re-distribution or changing local stress as it grew longer, then a Paris based model should be used in order to take advantage of β (geometry and

load/constraint factor) that is incorporated into this model. With the incorporation of β, derived from the QF for the location of interest into the model of the data from the coupon test (which do not have the complex load re-distribution or changing local stress found in the component), the changing β is used to predict CG curves at the detail using the effective block approach, or for different spectra when combined with the relative spectra method shown above. The following describes the procedure used to apply the effective block approach to an example detail in the F/A-18 wing where growth was not exponential over the life of the crack:

• QF data for an F/A-18 wing location taken from two full-scale fatigue tests for the same location are presented in Figure 14 [27]. This is an example where the load was being shed as the cracks grew longer. These results were for two different F/A-18 WRBM spectra. Note that the growth characteristics are very similar but they are not exponential.

• The Paris based model constants C (m taken as 2) were obtained from generic coupon tests of the spectra applied to the wings. In this case the results from more than one wing test could be used since the configurations of the wings were similar, and coupon data were available for each spectra.

• For each [a, t] data point obtained from the QF of the cracks in the location of interest the term σβ was calculated using the following re-arranged Paris relationship:

from (5) and (7) ata

C

m

ref πδδβσ 11

1

=

(10)

where C and m were the Paris constants determined from coupon tests for each wing test/s load spectrum.

• Plotting all the σβ, and fitting a curve through the data, gave a σβ(a) curve representing the unique local stress, geometry and loading boundary conditions for this critical location, as shown in Figure 15.

• Select an initial crack size, or a crack size from which to extrapolate, and also the critical stress intensity for final fracture.

• Then the CG a vs t prediction for the location of interest can be determined by integrating the Paris equation (5 and 7), using the derived function σβ(a) for the location of interest, along with the estimated C constant for the spectrum of interest giving the predictions shown in Figure 14.

If the spectrum of interest is the same as that applied to the wing, then the prediction should match the original QF data (see the two spectra predictions in Figure 14). However, if CG under a different spectrum (not the same spectrum at a different reference stress) is required, then a prediction can be made based on the C constant for the different spectrum (see examples for one of the spectra with its C coefficient adjusted by ±20% in Figure 14), which show a lack of sensitivity to small errors in the C coefficient. When the resulting distribution σβ(a) needs to be extrapolated to cover longer (or shorter) crack lengths, then it will be necessary to either gather data for longer cracks from testing, use FE analyses, or make estimates using handbook solutions or other methods. For the example in Figure 15, a conservative (constant) extrapolation of the σβ is shown at the deepest crack depth measured.

0.01

0.1

1

10

100

-10000 -5000 0 5000 10000 15000 20000

Cra

ck le

ngth

, a (m

m)

- -


QF data for Hole #8 (Wing test 1)QF data for Hole #8 (Wing test 2)Prediction for Hole #8 (Wing test 1)Prediction for Hole #8 (Wing test 2)Prediction for Hole #8 (Wing test 2 +20%)Prediction for Hole #8 (Wing test -20%)

0.01

0.1

1

10

100

-10000 -5000 0 5000 10000 15000 20000

Cra

ck le

ngth

, a (m

m)

- -


QF data for Hole #8 (Wing test 1)QF data for Hole #8 (Wing test 2)Prediction for Hole #8 (Wing test 1)Prediction for Hole #8 (Wing test 2)Prediction for Hole #8 (Wing test 2 +20%)Prediction for Hole #8 (Wing test -20%)

Figure 14 Plot of crack growth data from QF of a critical hole in the F/A-18 wing [27] (plotted against time using

an arbitrary reference crack length of 0.25 mm), including ‘effective block approach’ predictions for both the tested spectra (wing test 1 and wing test 2) for direct comparison with the QF data, and example predictions for a more severe spectrum (wing test 2 +20% CG rate) and a less severe spectrum (wing test 2 -20% CG rate).

0

100

200

300

400

500

0.01 0.1 1 10


Stre

ss x

Bet

a di

strib

utio

n, s

ß(a)

(MP

a)




0

100

200

300

400

500

0.01 0.1 1 10


Stre

ss x

Bet

a di

strib

utio

n, s

ß(a)

(MP

a)




Figure 15 Plot of σβ data derived from QF of the aft closure rib hole no. 8 location in the F/A-18 wing [27], showing

an ‘interpolated’ best fit curve through the data for use in the Paris based CG model in the effective block approach.

2.7 The limitations of these approaches

To achieve a robust application of the proposed method for the F/A-18 lifing of areas poorly represented in the full-scale testing, several issues have been identified that need consideration:

• A high degree of confidence in the CG rate derived from the coupons is necessary. Here the quality of the QF used was high. This gave good confidence in the data. Both the Frost & Dugdale and Paris based models have been shown to fit the low Kt coupon QF

data well. A more challenging test will be to collapse QF data from high Kt coupon specimens such as those shown in Figure 4. The data in Figure 4 suggests that this will not be a problem.

• A high degree of confidence in predicting CG rates for the same spectrum applied at different stress scales is needed, and in the cases presented here very good prediction of the stress scaling has been achieved.

• Error may occur if the derived CG rate constant is applied to significantly thinner material thicknesses due to plane stress and strain state effects. For the F/A-18 lifing work, the coupon tests generally represent the thickness of the wing structure.

• The applicability of the method to different crack and component geometries needs further investigation, although the Paris based model can account for different crack and component geometries with respect to the stress intensity at the crack tip, which is expected to account for most of the effects that different crack and component geometries can have on the CG rates. QF data at each critical location of interest is therefore important in order to produce the most reliable life prediction by giving the correct σβ(a) solution; alternatively a high fidelity FE analysis could be used.

• Predicting spectra CG rates for which coupon CG data does not exist may be problematic. Coupon tests for any new spectra are highly desirable in order to reduce any error that may be introduced by relying on current available software algorithms to predict the relative CG rate differences.

Eventually, it will be necessary to test the robustness of the approach across a wider range of spectra, materials and geometries. A dedicated program of testing is being undertaken. 2.8 Potential for wider application

The approach is being applied to the F/A-18 outer wing and to several components of the empennage for lifing and the setting of inspection intervals. It has also been used to life items made from materials other than the AL7050 T7451. In the case of the empennage, this was the AF1410 steel of the horizontal stabilator spindle [23][28]. There appears to be significant potential for this method to also be applied to other situations, where the loading spectra can be represented by repeating blocks of VA sequences. Some of these other applications may be:

• Spectra with �over loads�: This approach ought to be applicable if the overload event/s are repeated every block. An example of this is given in [1] where the CG was exponential over most of the specimen�s life even though the growth rate varies considerably between overloads.

• Cracking through residual stress fields: Where representative coupon data are available then it should be possible, using the σβ(a) solution from the coupon data, to predict CG for the same spectrum in aircraft components with surface treatments, such as shot-peening.

3. Conclusion

An effective block approach combined with either a Frost & Dugdale or a Paris (LEFM) based crack growth (CG) model, has been used to aid in the lifing of a number of F/A-18 components. The approach appears to offer significant improvement compared to the currently available models,

primarily because the CG rates are derived from variable amplitude (VA) coupon tests using the spectrum of interest or one that is relatively similar, combined with QF results from full-scale fatigue tests, rather than relying on tools that translate constant amplitude (CA) CG rates from material databases into CG rates for VA applications. The approach was able to use VA data to predict the CG of other spectra and QF data from two full-scale fatigue tests to predicted geometry/load shedding effects. The effective block approach provides an alternative means to interpret full-scale and coupon fatigue test data, and produces reliable life predictions for a range of scenarios, particularly were items of a full-scale fatigue test have either not been adequately tested or have been tested with a spectrum different from the target spectrum. This is a very economical and important capability where only single full-scale fatigue tests can be afforded and the results of the test do not extend to the clearance of all structure. The method has shown that combining results from detailed QF of full-scale F/A-18 fatigue tests with coupon test QF results has allowed the following goals to be achieved:

• The ability to accurately extrapolate beyond typical crack lengths obtained during testing; • The prediction of cracking under the same load spectrum at different �reference� stresses; • The prediction of changes in the CG that can be caused by geometry/load

shedding/residual stress conditions; and • An ability to compare different spectra at similar loading severity.

4. References

1. Barter S, Molent, L, Goldsmith N and Jones R. (2005) An experimental evaluation of fatigue crack

growth, Journal Engineering Failure Analysis, 12(1), 99-128. 2. Frost N E and Dugdale D S. (1958) The propagation of fatigue cracks in sheet specimens. Journal of

the Mechanics and Physics of Solids, 6(2) 92-110. 3. Speaker S M, Gordon D E, Kaarlela W T, Meder A, Nay R O, Nordquist F C and Manning S D. (1982)

Durability method development, volume VIII – Test and fractography data. Air Force Flight Dynamics Laboratory, Wright-Patterson Air Force Base, AFFDL-79-3118.

4. Molent L, Dixon B, Barter S, Medved J and Nguyen Q. (2005) The FINAL program of enhanced teardown for a fighter aircraft., proc. ICAF 2005, Hamburg, Jun 8-10.

5. Barter S A. (2003) Fatigue crack growth in 7050T7451 aluminium alloy thick section plate with a surface condition simulating some regions of F/A-18 structure. DSTO-TR-1458, Department of Defence, Defence Science and Technology Organisation, Melbourne, Australia.

6. Barter S and Huynh J. (2005) Fatigue crack growth in 7050-T7451 aluminium alloy open hole coupons. DSTO-TR-1XXX, Department of Defence, Defence Science and Technology Organisation, Melbourne, Australia (in publication).

7. UK Ministry of Defence, (2003) Defence Standard 00-970 Part 1, Issue 3, Design and Airworthiness Requirements for Service Aircraft, Structures.

8. JSSG-2006. (1998) Joint Service Specification Guide, Aircraft Structures, Department of Defense, USA. 9. Anon. (2005) F/A-18 fuselage/empennage through life management strategy. RAAF,

ASI/A21/03016 Pt1 (42). 10. Anon. (2003) Application of Def Stan 970 to the Aft Fuselage/Empennage. ER-F18-51-ASM343,

Revision 2, AeroStructures, Melbourne. 11. Molent L, Sun Q and Green A J. (2005) The F/A-18 fatigue crack growth data compendium. DSTO-

TR-1677, Department of Defence, Defence Science and Technology Organisation, Melbourne, Australia.

12. Forth S C, Newman J C Jr. and Forman R G. (2003) On generating fatigue crack growth thresholds.

International Journal of Fatigue,25, 9�15. 13. Schijive J. (1996) Fatigue crack growth under variable-amplitude loading, ASM Handbook Volume 19:

Fatigue and Fracture. Lampman S. (Technical Editor), ASM International, 110-133. 14. Pell R A, Molent L and Green A J. (2004) The fractographical comparison of F/A-18 aluminium alloy

7050-T7451 bulkhead representative coupons tested under two fatigue load spectra at several stress levels. DSTO-TR-1547, Defence Science and Technology Organisation, Melbourne, Australia.

15. Paris P C. (1964) Fatigue � an interdisciplinary approach. In proceedings of the 10th Sagamore Conference, Syracuse University Press, Syracuse, New York, p 107.

16. Sih G C. (1973) Handbook of Stress Intensity Factors for Researchers and Engineers, Institute of Fracture and Solid Mechanics, Lehigh University, Bethlehem, Pennsylvania.

17. Tada H, Paris P and Irwin G. (1985) The Stress Analysis of Cracks Handbook. Paris Productions Incorporated, St. Louis, Missouri.

18. Rooke D P and Cartwright D J. (1976) Compendium of Stress Intensity Factors, Her Majesty�s Stationary Office, London.

19. Murakami Y. (Editor-in-Chief) (1987) Stress Intensity Factors Handbook, Pergamon, New York. 20. Molent L, Barter S, Jones, R and Pitt S. (2005) Recent Australian developments in fatigue life

assessment, Submitted to International Journal of fatigue. 21. Barter S A. (2003) Fatigue crack growth in 7050T7451 aluminium alloy thick section plate with a

surface condition simulating some regions of F/A-18 structure. DSTO-TR-1458, Department of Defence, Defence Science and Technology Organisation, Melbourne, Australia.

22. Molent L, Barter S A and Green A. (2004) Comparison of two F/A-18 aluminium alloy 7050-T7451 bulkhead coupon fatigue tests. DSTO-TR-1646, Defence Science and Technology Organisation, Melbourne, Australia.

23. Cox A F and Barter S A. (2003) F/A-18 horizontal stabilator spindle AF1410 coupon testing program. DSTO-TR-1443, Department of Defence, Defence Science and Technology Organisation, Melbourne, Australia.

24. Harter J A. (2003) AFGROW users guide and technical manual. Air Vehicles Directorate, Air Force Research Laboratory OH, AFRL-VA-WP-TR-2003-XXXX.

25. Newman J C Jr. (1992) FASTRAN II � A Fatigue Crack Growth Structural Analysis Program, NASA Technical Memorandum 104159, 1992

26. Ghidella J R, Molent L and Madley W B. (2003) A Comprehensive Guide to the Fatigue Damage Assessment Computer Program CI89. DSTO-TR-1506, Department of Defence, Defence Science and Technology Organisation, Platform Sciences Laboratory.

27. Huynh J, Molent L and Barter S. (2004) F/A-18 inner wing lifing investigation. DSTO-TR-1641, Department of Defence, Defence Science and Technology Organisation, Platform Sciences Laboratory, Melbourne, Australia.

28. McDonald M and Molent L. (2004) Fatigue assessment of the F/A-18 horizontal stabilator spindle. DSTO-TR-1620, Department of Defence, Defence Science and Technology Organisation, Platform Sciences Laboratory, Melbourne, Australia.

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