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

2 Contents Introduction Method outline Underlying theoretical model Evolutionary feature recognition An application example Results & Analysis Conclusions and future work

January, 31, Introduction Modern CAD modellers make use of features

January, 31, Introduction Modern CAD modellers make use of features

January, 31, Introduction Modern CAD modellers make use of features Han (2000)

January, 31, Introduction Modern CAD modellers make use of features Han (2000) Laakko & Mäntylä (2000)

January, 31, Introduction Modern CAD modellers make use of features Han (2000) Laakko & Mäntylä (2000) Cavendish (1995)

January, 31, Introduction Modern CAD modellers make use of features Han (2000) Laakko & Mäntylä (2000) Cavendish (1995) Pernot (2003)

January, 31, Introduction Modern CAD modellers make use of features Han (2000) Laakko & Mäntylä (2000) Cavendish (1995) Pernot (2003) Vergeest (2001)

January, 31, 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

January, 31, Method outline Underlying theoretical model Features have a nurbs representation Parameter control occurs through displacement of the control points

January, 31, Method outline Underlying theoretical model Features have a nurbs representation Parameter control occurs through displacement of the control points

January, 31, Method outline Underlying theoretical model Features have a nurbs representation Parameter control occurs through displacement of the control points

January, 31, Method outline Underlying theoretical model Features have a nurbs representation Parameter control occurs through displacement of the control points

January, 31, Method outline Evolutionary 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

January, 31, Method outline Evolutionary 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’

January, 31, Method outline Evolutionary 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

January, 31, An application example Bottle Gen #0

January, 31, An application example Bottle Gen #1

January, 31, An application example Bottle Gen #2

January, 31, An application example Bottle Gen #3

January, 31, An application example Bottle Gen #4

January, 31, An application example Bottle Gen #5

January, 31, 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

January, 31, Results and analysis Feature type (# of params) Correctly identified Incorrectly identified Not identifiedTotal 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%) 292 Total2290 (92%)167 (7%)43 (2%)2500 Table 1: Result for different feature types

January, 31, Results and analysis Population size/ Selection size Total 5% % % % % Total381 (76.2%)452 (90.4%)468 (93.6%)489 (98.8%)500 (100%)2290 Table 2: Result for different population sizes

January, 31, 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.

January, 31, 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!