achieve robust and manufacturable composite design systemes... · catia v5, excel, abaqus, etc...

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GPDIS_2013.ppt | 1

Achieve Robust

and Manufacturable

Composite Design

Mahesh Turaga

Bill Brothers

Dassault Systemes

Global Product Data Interoperability Summit | 2013



GPDIS_2013.ppt | 2

Courtesy of Google Images

http://www.google.com/url?sa=i&rct=j&q=broken workflow funny pictures&source=images&cd=&cad=rja&docid=UugUuw0WNEN-BM&tbnid=BRcGEqR6qw0NcM:&ved=0CAUQjRw&url=https://jira.atlassian.com/browse/JRA-25548?focusedCommentId=468793&ei=804KUvSSGYGkigLFyoGYAQ&bvm=bv.50500085,d.cGE&psig=AFQjCNFrpmA0bLm5UOJ0L50fTA5XU3aLFw&ust=1376493679680774

http://www.google.com/url?sa=i&rct=j&q=imap+workflow&source=images&cd=&cad=rja&docid=xmRv9b7LJPIyqM&tbnid=5lwPofAq4Xz5qM:&ved=0CAUQjRw&url=http://blogs.vmware.com/orchestrator/&ei=kO8jUqqMCez94APjuICgAQ&bvm=bv.51495398,d.cWc&psig=AFQjCNHgHnqN21Sbs78-b8yhEhtVlYKdvg&ust=1378173121499526




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Improving productivity and innovation

Reduce Engineering Cycle Times • AAI-Textron Corp – UAS (Unmanned Aircraft System)

conceptual design process reduced from 5 days to 10

minutes per design iteration.

3

Over 2,500 Aircraft &

Associated Systems

evaluated over a weekend




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Improving productivity and innovation

Haliburton*

• 98% reduction in engineers non-

productive time

• Cut Test Validation from 1 day to 15

minutes

• Establish “best practices”

Rolls-Royce*

• Created Teamwork

• More efficient Communication

• Data Driven Decisions

Baker Hughes*

• Analysis reduced from 2 Months to 2

Days

*2010, 2011 DS Simulia Community Conference (SCC) papers

#

# Realistic Simulation News, September/October 2011




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Isight Technology Overview

• Integration & execution of current tools

• Automation of simulation workflows • Full suite of powerful exploration

tools Optimization Design for Six Sigma Design of Experiments Reliability and Robustness Approximation models Nested exploration / MDO

• Interactive visualization to analyze alternatives

• SEE for enterprise




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Problem Description

• Design a reliable, robust and manufacturable winglet on time and within budget

Critical Business Issue

• Non integrated process, too many variables in too many tools

Reasons

• Integrated process that automatically drives variables across tools and manages results

Solution

• Manufacturable & optimized design in a few hrs vs a few months

Impact

Number of Plys*: [8-16]

Ply Angles: [0,+45,-45,-90,+90]

Seed Points: 31

CATIA V5, Excel, Abaqus, etc

TSAIW < 1.0

Cost

Weight

> 74,613 possibilities




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The Workflow




GPDIS_2013.ppt | 8

The Result

Base Optimized

Layup [0/90/45/-45]2s [0/90/90/30/-

30]s

No of plies 16 10

Time ~ 2 weeks ~ 2 min

% savings

Weight 37

Cost 50

Original Optimized

Deflection

(< 5)

5.9 4.1

Max strain

(< 30)

30.9 26.8

Tsai Wu

(< 1.0)

0.74 0.34

Units: in, lb




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The composite workflow

Winglet Design in CPD

Composite Layup, Design

in CMA

Draping Simulation, Structural Analysis

Cost & Weight

Optimized Design Workflow

automation, Optimization & Reliability using

Isight

Lay-up,

Seed Point




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LEGOS




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Component: Simcode

Input Execution Output




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Component: Simcode





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Component: Simcode





GPDIS_2013.ppt | 14

14

Open Component Based Architecture for Simulation Integration

Dymola




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Winglet Workflow - CAD model




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Winglet Workflow – CATIA V5 model




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Winglet Workflow – The optimization problem




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Winglet Workflow – Multi-objective optimization

• Minimize Cost, Weight Objective

• Deflection, Tsai-wu, Strain Constraints

• Ply angles: 0, +/-(15, 30,45,60,75), 90 & seed points

Variables

• 16 plies, [0/90/45/-45]2s Base Design




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Seed Point and Draping

• Seed Point: The point of first application

point for a composite ply

• Draping: The process of applying a flat sheet

material to a mould surface. The material

must “shear” to conform to a curved surface.




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Draping initiated on cap Draping initiated on cylinder

Courtesy of Google images




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Seed point

Seed point

• Blue: < 50% Maximum Strain

• Yellow: 50-100% Maximum Strain

• Red: > 100% Maximum Strain




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Seed Point Variation

• Strain < 30 deg Constraints

• Seed Points ~ 14 Variables

• 16 plies, [0,90,45,-45]2s Base Design Case 1 • Seed point located

anywhere on the surface

Case 2 • Seed point restricted to

the leading edge




GPDIS_2013.ppt | 23

Winglet Workflow – Ply lay-up variation

• Minimize Cost, Weight Objective

• Deflection, Tsai-wu Constraints

• Ply angles: 0, +/-(15, 30,45,60,75), 90, No of plies 8-16

Variables

• 16 plies, [0,90,45,-45]2s Base Design




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Optimization Technique Classification

• Gradient Techniques • Generally assume the design space is continuous

• Direct Methods Techniques • Use only function evaluations

• Typically evaluate a performance index in

some pattern around the base point.

• Exploratory Techniques • Look for global optimum

Exploratory

Direct Gradient




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Multi Island GA

Optimization Technique used

• Exploratory technique

• Genetic algorithm

• Well-suited for discontinuous design spaces

• Ran about 216 design points (analysis runs)




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Winglet Workflow - Cost Analysis




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Cost and Weight




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The data behind the workflow

Plies

Ply angles

Seed points

Possibilities

16

7

31

> 74613




GPDIS_2013.ppt | 29

Best Point

Results




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Case 1: Seed point anywhere on the surface( no

constraints on ply angles)

Results

• Base Configuration

• [0/90/45/-45]2s

• Seed point location 1

• No of plies 16

• Optimized Configuration

• [30/90/90/45/90/90/-45/45/90/45]


• No of plies 10

Original Optimized

Deflection 5.997 4.3

Max strain 30.92 26.189

Tsai Wu 0.745 0.8875

Cost 146428 72928

Weight 3.62 2.28

Units: in, lb, $

13

1

% savings

Cost 50

Weight 37




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Case 2: Seed point on the leading edge(balanced

and symmetric lay-up constraint)

Results

• Base Configuration

• [0/90/45/-45]2s


• No of plies 16

• Optimized Configuration

• [0/90/30/-30]s


• No of plies 8

Original Optimized

Deflection 5.99 4.48

Max strain 30.9 29.6

Tsai Wu 0.745 0.706

Weight 3.64 1.82

Cost 146428 53952

5

16

% savings

Weight 50

Cost 63

Time 1 hr 54 min

# of runs 216




GPDIS_2013.ppt | 32

History Plots

Results

Infeasible design

Feasible design




GPDIS_2013.ppt | 33

Scatter Plots

Results

Design Lay-up No of

plies

Seed Pt

Base [0/90/45/-45]2s 16 5

Optimized 1 [0/90/30/-30]s 8 16

Optimized 2 [0/90/90/45/45/-45/]s 12 6

Optimized 3 [0/90/90/30/-30]s 10 16

5

16

20

6

Optimized Designs




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Surrogate Models

Results

• Mathematical models that approximate the

relationship between a set of inputs and

outputs

• Reduce evaluation time for an exact analysis

F(x) = a0 + S bixi + S ciixi2+ S cijxixj (i<j) + S dixi

3 + S

eixi4




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Surrogate Models

Results




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Surrogate Models

Results




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Winglet Workflow – Reliability and Robustness




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Monte Carlo Analysis

Winglet Design - Reliability and Robustness

• Sample Random Sampling

• Standard deviation of 1.0

• Ply angles, Ply thickness




GPDIS_2013.ppt | 39

Pareto Plots

Results

• Measuring the effect of input parameters




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Winglet Design – Manufacturable, Reliable and Robust

Design Lay-up No of plies Seed Pt

Base [0/90/45/-45]2s 16 5

Optimized Design 1 [0/90/30/-30]s 8 16

Optimized Design 2 [0/90/90/45/45/-45/]s 12 6

Optimized Design 3 [0/90/90/30/-30]s 10 16

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 50 100 150 200 250

Tsa

i-w

u F

ail

ure

In

de

x

Design Variations

Design 3 (10 plies)

Design 2 (12 plies)

Design 1 (8 plies)

Initial Best

Reliable, Robust




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Summary

Reliable Robust winglet design obtained from 100s of possibilities

Optimal seed point location determined using CMA

Process automation of various tools driving the data behind them




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Acknowledgements

• Krishna Akula, Dassault Systemes

• Etienne Ardouin, Dassault Systemes

• Deepak Goyal, Dassault Systemes




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Contact Info

• Mahesh Turaga

• [email protected]

• Bill Brothers

• [email protected]

mailto:[email protected]

mailto:[email protected]

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