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Evolutionary Recognition of Features from CAD Data

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Book cover Simulated Evolution and Learning (SEAL 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1585))

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Abstract

This paper proposes a method based on evolutionary computation for recognizing features of CAD data. Feature-based chromosome scheme is developed in which its locus corresponds to two features of CAD data provided by using the Boundary Representation method. The efficiency of the proposed method is shown through experimental results.

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References

  1. Chu-Chai Henry Chan: Artificial Neural-Network-Based Feature Recognition and Grammar-Based Feature Extraction to Integrate Design and Manufacturing, Ph.D Disser.. Univ. of Iowa. 1994.

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  2. Yasuhiro Tsujimura. Mitsuo Gen and Masaki Hiji: Feature Recognition of CAD Data Using Evolutionary Computation, Proc. of 1997 Spring Meeting of JIMA, pp.164–165. 1997. (in Japanese)

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  3. Yasuhiro Tsujimura. Mitsuo Gen and Masaki Hiji: Evolutionary Feature Recognition of CAD Data Employing Boundary Representation Method, Proc. of Third International Symposium on Artificial Life, and Robotics, pp.39–42, 1998.

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  4. Nikkei CG ed.: New Foundation of CAD, Nikkei BP, 1996. (in Japanese)

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  5. Mamoru Hosaka and Toshio Sata: Integrated CAD/CAM System, Ohm Pub., 1994. (in Japanese)

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  6. Yukinori Kakazu and Masasi Furukawa: Shape Disposal Engineering for CAD/CAM/CG. Morikita Pub., 1995. (in Japanese)

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  7. Mitsuo Gen and Runwei Cheng: Genetic Algorithms and Engineering Design, John Wiley & Sons, New York, 1997.

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© 1999 Springer-Verlag Berlin Heidelberg

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Tsujimura, Y., Gen, M. (1999). Evolutionary Recognition of Features from CAD Data. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_36

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  • DOI: https://doi.org/10.1007/3-540-48873-1_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65907-5

  • Online ISBN: 978-3-540-48873-6

  • eBook Packages: Springer Book Archive

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