An Evolutionary Strategy for Free Form Feature Identification in 3D CAD Models

WSCG’07 conference

Thomas R. Langerak, Joris. S.M. Vergeest, Huaxin Wang, Yu Song

January, 31, 2007

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Contents

• Introduction• Method outline

• Underlying theoretical model• Evolutionary feature recognition

• An application example• Results & Analysis• Conclusions and future work

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Introduction

• Modern CAD modellers make use of features

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Introduction

• Modern CAD modellers make use of features

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Introduction

• Modern CAD modellers make use of featuresHan (2000)

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Introduction

• Modern CAD modellers make use of featuresHan (2000) Laakko & Mäntylä (2000)

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Introduction

• Modern CAD modellers make use of featuresHan (2000) Laakko & Mäntylä (2000) Cavendish (1995)

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Introduction

• Modern CAD modellers make use of featuresHan (2000) Laakko & Mäntylä (2000) Cavendish (1995)

Pernot (2003)

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Introduction

• Modern CAD modellers make use of featuresHan (2000) Laakko & Mäntylä (2000) Cavendish (1995)

Pernot (2003) Vergeest (2001)

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Introduction

• Modern CAD modellers make use of features• Feature recognition is needed when:

• High-order information is not available• Translation of the high-order information to

another domain is required

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Method outlineUnderlying theoretical model

• Features have a nurbs representation• Parameter control occurs through displacement

of the control points

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Method outlineUnderlying theoretical model

• Features have a nurbs representation• Parameter control occurs through displacement

of the control points

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Method outlineUnderlying theoretical model

• Features have a nurbs representation• Parameter control occurs through displacement

of the control points

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Method outlineUnderlying theoretical model

• Features have a nurbs representation• Parameter control occurs through displacement

of the control points

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Method outlineEvolutionary feature recognition

• Feature in a CAD model are recognized using template features

• A succesful recognition occurs when the ‘distance’ between a template feature and the target shape is minimal

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Method outlineEvolutionary feature recognition

• Evolutionary computation: treating computational problems as cases of natural evolution

“Survival of the fittest”

• Features can be viewed as organisms, with parameters as ‘genes’

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Method outlineEvolutionary feature recognition

• Consecutive generations of a feature population are computed, using the shape similarity as a fitness indicator.

• The populations ‘evolve’ to an optimal solution

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An application exampleBottle

Gen #0

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An application exampleBottle

Gen #1

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An application exampleBottle

Gen #2

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An application exampleBottle

Gen #3

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An application exampleBottle

Gen #4

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An application exampleBottle

Gen #5

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Results and analysis

• 2500 tests conducted

• Target shapes were simulated by instantiating features on a flat surface and distorting them with Gaussian noise.

• Different values for the feature population size were used

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Results and analysis

Feature type(# of params)

Correctlyidentified

Incorrectlyidentified

Not identified Total

Plane (6) 308 (96%) 13 (4%) 1 (0%) 322

Bump (8) 291 (94%) 14 (5%) 3 (1%) 308

Ridge (9) 274 (88%) 32 (10%) 5 (2%) 311

Cross (9) 324 (96%) 8 (2%) 4 (1%) 336

Step (8) 296 (95%) 14 (5%) 1(0%) 311

Wave (10) 212 (69%) 73 (24%) 23 (7%) 308

Blend (8) 301 (96%) 9 (3%) 2 (1%) 312

Crown (10) 284 (97%) 4 (1%) 4 (1%) 292

Total 2290 (92%) 167 (7%) 43 (2%) 2500

Table 1: Result for different feature types

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Results and analysis

Population size/Selection size

1000 1500 2000 2500 3000 Total

5% 78 91 96 100 100 463

10% 83 96 100 100 100 479

20% 82 94 95 99 100 470

30% 76 88 88 97 100 449

40% 62 83 89 93 100 429

Total 381 (76.2%) 452 (90.4%) 468 (93.6%) 489 (98.8%) 500 (100%) 2290

Table 2: Result for different population sizes

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Conclusions and future work

• The method is able to identify features in a reasonable time (approx. 2 minutes)

• The method needs to be validated for more complicated situations.

• Work is being done to use the method for feature recognition and automatic feature type construction.

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Thanks and acknowledgements

The research project DIT.6240 is supported by the Technology Foundation STW, applied science division of NWO and the technology program of the Ministry of Economic Affairs, The Netherlands.

Thank you for listening!