Component quality and validation by design
“Avio snapshots in CRESCENDO”
Novembre 2007
Jan. 2006The information contained in this document is Avio S.p.A. proprietary information and is disclosed in confidence. It is the property of
Avio S.p.A. and shall not be used, disclosed to others or reproduced, without the express written consent of Avio S.p.A..
15 locations worldwide 4,800 employeesR&D investments:11.5% of revenues
A Long Heritage in Aerospace Since 1908
3
AVIO BUSINESS ACTIVITIES
EJ200
Ariane 5
2004 revenues €1,219m
14.5%8.5%29%
48%
GE90-115B
CIVIL
MILITARYEngines and MRO
SPACE
CIVILMRO
Strong Customer Relations
… governmental institutions
Avio components are installed on 60% of jet engines powering commercial fleets worldwide
5
SaM146
Russian Regional JetTrent 900
Marine One
Boeing 787 Dreamliner
GEnx
Avio is the Italian Partner in the Most Important Aerospace Programmes
A380
CT7-8 Growth
Vega launcher
Ariane 5
6
Tight supply chains
Business Strategic Drivers
Green aero-engines
Advanced materials
Prognostics &Diagnostics
Integrated M&ROsolutions
Collaborative and Robust process
7
CompetitivenessCompetitiveness
• Affordability
• Life Cycle Cost
• Performance
Environment (Green Engine)Environment (Green Engine)
• Low Emissions
• CO2: -20%
• NOx: -80%
• Low Noise
• Perceived noise (-18 EPNdB)
• Ultra GreenUltra Green
• Ultra Secure
• Highly Cost EfficientHighly Cost Efficient
• Highly Time Efficient
• Highly Customer Oriented
• 22nd Century Vision
NEW E
NGINE
NEW E
NGINE
ARCHITECTURES
ARCHITECTURES
ACARE R&T Target – Vision 2020
SF
IDE
8
Avio in R&T A.A.T. European ProjectsAvio in R&T A.A.T. European Projects
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
ANTLE/POA
CLEAN
VIVACEVITAL
NEWAC
FP7
Variable GeometryIntermediate Pressure Turbine
Low Nox Combustor
Low weight Low noise
Low Pressure Turbine
InnovativeCombustor
DREAMLP Turbinefor Open Rotor
CLEAN SKYJTI
Distributed designMultidisciplinary/Multienterprise models
TRLNational programs
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FP7 Technology Strategy – Level 2FP7 Technology Strategy – Level 2
GOLD
201420122010
VITAL
CRESCENDO
LEMCOTEC
DREAM
Emissions –NOx
CO2
Noise
Engine design
Simulation
Operational support
Breakthrough technologies
NACRE
E Break-Comp
2008
Module Validator in engine
NEWAC
Call 2
OPENAIR
Call 3 Call 4
VIVACE
EIMG Property
10
CRESCENDO
Collaborative & Robust Engineering using Simulation CapabilityEnabling
Next Design Optimisation
Ambito : EU Research Framework VIIPeriodo: 2009 - 2012Partners: Main European Aerospace Manufacturers
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Follow up fromCollaborative Robust Process previous research projects
- Knowledge Enabled Engineering: Design Practice Management Knowledge Access Point
- Collaboration working processCollaboration Hub EnvironmentMultidisciplinary CAD/CAE analysis
- Manufacturing DesignCAD 3D extensive modelingNC Simulation
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More analysis at aircraft and component level to eliminate risk early in the design process – “Robust Prelim Design”
Analysis based test strategy, planning & correlation to reduce the need for repeat testing – “Virtual Test”
Fixed design, probabilistic analysis for service and aftermarket assessment of maintenance / cost – “Product Life-Cycle Analysis”
More sophisticated, multi-physics analysis to accurately predict component and functional behaviour e.g. energy, performance, mechanical integrity, …. – “Virtual product”
Use of analysis based optimisation and robust design to enable rapid definition at aircraft and component level – “Design by Analysis”
Certification based on analysis, simulation & modelling – “Virtual Certification”
CRESCENDO – Areas of interest
EIMG Property
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Avio Areas of interest
Validation by design: Analysis models to further reduce physical tests
Quality by design: Capability integration to reduce non quality costs
Performance design: simulation of overall engine architecture to maintain leadership in component design
Design for reliability: Optimization using service data
Manufacturing design:Machining measurement and inspection optimization
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Design for Reliability
FMEA Failure mode
Allowed event probabilityAllocation
SeverityParts Classification
Weak points:
• Need of heavy product experience people
• Uncertainties in allocation weight estimation by using not sophisticated
algorithms
Regulations
Failure effect
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Design for Reliability
FMEA Failure mode
Allowed event probabilityAllocation
SeverityParts Classification
Regulations
Failure effect
Structured forproduct typescomponent typesmission phase
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Identify Key Characteristics / CTQs / Limits
Flow Down to subsystems / components
Understand variability of the Xs
Build Transfer Functions
RD / DFR - optimize to reduce p(d)
Tolerance the Xs
Validate the design
Issue the design
Quality by Design : DFSS Methodology
17
Identify Key Characteristics / CTQs / Limits
Flow Down to subsystems / components
Understand variability of the Xs
Build Transfer Functions
RD / DFR - optimize to reduce p(d)
Tolerance the Xs
Validate the design
Issue the design
Identify Key Characteristics / CTQs / Limits
Flow Down to subsystems / components
Understand variability of the Xs
Build Transfer Functions
RD / DFR - optimize to reduce p(d)
Tolerance the Xs
Validate the design
Issue the design
Integrated Models for critical parameters (X’s) evaluations and analysis of the Design space (Y’s)
Factor Probability
Distributions
PredictedY Probability Distribution
Transfer Function
Y = f (X1, X2, … Xm)
X1
X2
XM
Y
0. 70 0. 85 1. 00 1. 15 1. 30
A1
0. 74 0. 89 1. 04 1. 19 1. 34
A1
5. 00 6. 84 8. 67 10.51 12.34
A1
0. 90 0. 95 1. 00 1. 05 1. 10
A1
Factor Probability
Distributions
PredictedY Probability Distribution
Transfer Function
Y = f (X1, X2, … Xm)
X1
X2
XM
Y
0. 70 0. 85 1. 00 1. 15 1. 30
A1
0. 74 0. 89 1. 04 1. 19 1. 34
A1
5. 00 6. 84 8. 67 10.51 12.34
A1
0. 90 0. 95 1. 00 1. 05 1. 10
A1
Quality by Design : Transfer Functions
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Analysis
Quality by Design : Uses of Transfer Functions
Determine which factors are
important in the system
Sensitivities
Prediction
Build equations that can be used to
quickly and inexpensively predict
system behavior at new operating points
and/or conditions
Optimization
Probabilistic Design
Find the set of factor values that optimizes
the mean while minimizing the
sensitivity to variation
Robust Design
Design for Reliability
Legend
0.524
0.526
0.528
0.53
0.532
0.534
245 250 255 260 265 270 275
dTemp_Melt
Sf
55
60
65
70
75
80
245 250 255 260 265 270 275
dTemp_Melt
pre
ss_to
_fi
ll
16
16.5
17
17.5
245 250 255 260 265 270 275
dTemp_Melt
tim
e_cycle
2.14
2.16
2.18
2.2
2.22
245 250 255 260 265 270 275
dTemp_Melt
cost_
tota
l
0.505
0.51
0.515
0.52
0.525
0.53
0.535
0.54
60 65 70 75 80 85
dTemp_Mold
Sf
62
64
66
68
70
72
74
60 65 70 75 80 85
dTemp_Mold
pre
ss_to
_fi
ll
15.5
16
16.5
17
17.5
18
18.5
60 65 70 75 80 85
dTemp_Mold
tim
e_cycle
2.16
2.17
2.18
2.19
2.2
60 65 70 75 80 85
dTemp_Mold
cost_
tota
l
0.524
0.526
0.528
0.53
0.532
0.534
0 2 4 6 8 10
Time_Inj
Sf
50
60
70
80
90
0 2 4 6 8 10
Time_Inj
pre
ss_to
_fi
ll
12
14
16
18
20
22
0 2 4 6 8 10
Time_Inj
tim
e_cycle
2
2.1
2.2
2.3
2.4
0 2 4 6 8 10
Time_Inj
cost_
tota
l
0.45
0.5
0.55
0.6
35 40 45 50
Press_Pack
Sf
66.5
67
67.5
68
68.5
35 40 45 50
Press_Pack
pre
ss_to
_fi
ll
16.6
16.7
16.8
16.9
17
35 40 45 50
Press_Pack
tim
e_cycle
2.166
2.168
2.17
2.172
2.174
2.176
2.178
35 40 45 50
Press_Pack
cost_
tota
l
X1
246.1
260
273.9
X2
60
71.1
82.2
X3
1
5.5
10
X4
35
42.5
50
Sf press_to_fill time_cycle cost_total
0 33 66 99 132 165 198
Operability
FOD Tolerance
Clearance, % Diameter
Durability
Operating Line
Gas Path Leakage
Unbalance
Airf low
Maintainability
Dovetail Wear & Cracking
Efficiency
Speed
Rotor Thrust
Life Limited Component Part Life
LRU Removal Time
Module Integration
Module Critical Speed/Frequency
Fan Module Pareto
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3D Stack up Analysis
Numerical Control Machining,
Inspections
Manufacturing Design
Availability of an “intelligent” CAD Model enabling the specification and the piloting of a virtual manufacturing environments.
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Manufacturing Design: Extended Master Model (MM)
+
MM
Detail Drawing
“Product and Manufacturing Information”
Extended Master Model
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CMM
Export
Extended
Master Model
Manufacturing Design: Measuring & Inspection
Coordinate Measuring Machines