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Today, many aeronautical parts originally manufactured in metal become design and build in composites. According to our customer specifications, these parts are more and more loaded by their use, regroup more and more functions (a sub assembly designed in 20 metallic parts can lead to a 1 composite part) and must be as light as possible. In this way, we try to define an optimization process, which can give us the best in class, in composite, in metallic, and the hybrid part since the beginning of the project. Simulations have been carried out combining HyperStudy, OptiStruct and some third party software. The objective is to decrease the weight, respect our customer requirements (frequency, static loads, buckling,…), minimizing the cost and taking into account our industrial tools, from a metallic or composites point of view. In this way, some special shapes have been found and were applicable to a large variety of our products Finally, this process allows us to decrease the weight with an average of 40% on our product, whatever the solution (metallic, composite or hybrid), fulfilling the customer requirement with extra performances and respecting our industrial process.
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ASSOCIONS NOS TALENTS
AVRIL 2013
I N G ÉN I E R I E M É C A N I Q U E ET HYDRAULIQUE
SMART optimization process for
metal versus composites solutions
François MIGEOT – R&T eng. / FEA analyst
EATC – Munich 2014
ASSOCIONS NOS TALENTS
2
1. Introduction: MS Composites, a Groupe ADI Brand
2. Problem definition
3. Optimization process
1. Material free Shape optimization
2. Metal and composites « direct optimization » run
3. Hybrid « crossed optimization » run
4. Examples
5. Conclusion
AGENDA
ASSOCIONS NOS TALENTS
Turnover: 24,6 M€
Workers: 220 people on 3 factories (2 in France, 1 in
Morocco)
Materials and process:
• Thermoset & thermoplastic matrix
• Glass, carbon, aramids fibbers
• Autoclave, filament welling, RTM, press
Fields of activity:
• 73 % Aeronautics (Snecma, Daher, Latécoère)
• 13 % Defense (Thales)
• 12 % Medical (Trixell, GE Healthcare)
• 2 % Space and misc. (Sonaca)
1 Research and Technics center
3
INTRODUCTION: MS Composites, a Groupe ADI Brand
Turnover: 197 M€
1317 People Turnover: 24,6 M€
Workers: 220 people on 3 factories (2 in France, 1 in
Morocco)
Materials and process:
• Thermoset & thermoplastic matrix
• Glass, carbon, aramids fibbers
• Autoclave, filament welling, RTM, press
Fields of activity:
• 73 % Aeronautics (Snecma, Daher, Latécoère)
• 13 % Defense (Thales)
• 12 % Medical (Trixell, GE Healthcare)
• 2 % Space and misc. (Sonaca)
1 Research and Technics center
Turnover: 197 M€
1317 People
AD Industrie
in 2013
AD Industrie
in 2013
ASSOCIONS NOS TALENTS
Drive optimization through 3 points: Mechanical requirements (Customer requests)
Weight loss
Industrial (Mastered fabrication process / cost)
Manual optimization not satisfying all the points (up to 2) – Orange parts on the chart
below.
Necessity to develop a Shape, multi-Material, Automated, Robust and Trustful
(SMART) optimization process.
4
PROBLEM DEFINITION
Performances
Feasibility
Original
design
Fully optimised
part
Optimized part
Optimized part
ASSOCIONS NOS TALENTS
5
OPTIMIZATION PROCESS
Design
space
Based on the KP and Greedy algorithm:
Fully optimized part
Shape optimized part
“Material less”
Metallic optimization Composites
optimization
Hybrid optimization
Shape optimization of
the different parts w/o
material laws
Part decomposition, with
industrial constraints
Direct material run on
the shape optimized
parts
Crossed material run
on the shape optimized
parts
Fully optimized part
ASSOCIONS NOS TALENTS
6
PART DECOMPOSITION
Decomposition in elementary parts using:
R&T design rules
Industrial capacity
Define the assembly kind (fasteners, welds, glue, rivets, clinching…)
At this step, the most difficult point is to remain material less (all concept and parts
must be material independent)
Design
space
ASSOCIONS NOS TALENTS
7
MATERIAL FREE SHAPE OPTIMISATION
Definition of a “material less” optimization using HyperMesh, HyperStudy and In-House
Algorithm
Definition of the ML Criterion: Mechanical score x feasibility score
Feasibility score obtained with method department. It deals with design to cost
(fillet radius max and min, smallest parts machinable, max depth of grooves or
shoulders…)
% Mechanical vs original design obtained by combining some load ratio (NVH
obtained vs original, buckling, flexion, NL statics…)
Design
space
Shape optimized part
“Material less”
ASSOCIONS NOS TALENTS
8
“DIRECT OPTIMISATION” RUN
The shape defined, 2 other optimizations run :
With metallic materials:
o Different material (Steel, Titanum, Aluminum…).
o The variables applied on thickness, to ensure the metal sheet behavior (not using
Optistruct capabilities)
With Composites material:
o Different kind of fabrics (glass, carbon… / UD, vowen…).
o The variables applied on the ply angles, and the numbers of plies (not using
Optistruct possibilities)
Design
space
Fully optimized part
Shape optimized part
“Material less”
Metallic optimization Composites
optimization
ASSOCIONS NOS TALENTS
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
0 0,001 0,002 0,003 0,004 0,005 0,006 0,007
Go
bal
sco
re [
1]
Solution weight [T]
Metallic Global score vs Solution weight
Aluminum
Titanium
Steel
9
“DIRECT OPTIMISATION” RUN - Metallic
By applying materials on the obtained shape, it gives us a panel of solutions, depending of
the global score (technical and feasibility score) function of the weight.
On this direct optimization, some ways of development have been underestimated:
The steel, despite of its weight, can be a good solution and costly efficient.
ASSOCIONS NOS TALENTS
0
50000
100000
150000
200000
250000
300000
350000
400000
0,00E+00 4,00E-04 8,00E-04 1,20E-03 1,60E-03
Go
bal
sco
re [
1]
Solution weight [T]
Composites Global score vs Solution weight
Carbon UD
Carbon 2D
Carbon HM UD
Carbon HM 2D
10
“DIRECT OPTIMISATION” RUN - Composites
It allows us to make an efficient segregation depending on the weight and the requested
score, given by the customer specifications
Otherwise, some ways have been overestimated:
Standard modulus and unidirectional carbon will not fulfill the requirements.
ASSOCIONS NOS TALENTS
11
“CROSSED OPTIMISATION” RUN
Once the shape defined, the “crossed optimization” run is defined:
All materials can be applied in the model and used as variables
The thickness (in mm for metals, numbers and angles of plies for the composites) can
be applied and used as variables
Fully optimized part
Shape optimized part
“Material less”
Metallic optimization Composites
optimization
Hybrid optimization
Design
space
ASSOCIONS NOS TALENTS
12
“CROSSED OPTIMISATION” RUN
The hybrid solution gives us the benefits of both materials, without the drawbacks
By making this crossed optimization, the panel of solution becomes richer and allows the
Groupe ADi to propose hybrides solutions
ASSOCIONS NOS TALENTS
13
EXAMPLES
Test done on an Groupe ADi aeronautic engine part:
Test done on a MS-Composites aeronautic structural part:
Original
design
Manual
Optim. 1
Manual
Optim. 2
SMART
Optim.
metal
SMART
Optim.
Comp.
SMART
Optim
Hybrid
Global
score 100 90 100 121 220 135
cost 100 110 100 50 80 75
Weight 100 90 90 72 40 70
Original
design
Manual
Optim. 1
Manual
Optim. 2
SMART
Optim.
metal
SMART
Optim.
Comp.
SMART
Optim
Hybrid
Global
score 100 100 110 180 260 240
cost 100 110 100 40 85 77
Weight 100 80 80 60 40 55
ASSOCIONS NOS TALENTS
14
CONCLUSION
SMART Optimization process allows:
Solutions coming quicker.
All material available
Several proposition (low cost, high technical
perf. , easy to build…)
Maintain and add new material in the database.
The process highlights the underestimated or the unexplored ways of development.
ASSOCIONS NOS TALENTS
15
AVRIL 2013
I N G ÉN I E R I E M É C A N I Q U E ET HYDRAULIQUE
Q&A
Thank you for your
attention
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