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Smart optimization process for metal versus composites solutions

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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, …

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.

Published in: Engineering, Business, Technology

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  • 1. 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
  • 2. 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
  • 3. 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 AD Industrie in 2013
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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”
  • 8. 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
  • 9. 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 Gobalscore[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.
  • 10. 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 Gobalscore[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.
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. 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.
  • 15. 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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