Computer-Aided Operation Planning for an Actual Machine Tool Based on Updatable Machining Database and Database-Oriented Planning Algorithm

Shinji Igari; Fumiki Tanaka; Masahiko Onosato

doi:10.20965/ijat.2012.p0717

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IJAT Vol.6 No.6 pp. 717-723

doi: 10.20965/ijat.2012.p0717

(2012)

Paper:

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Computer-Aided Operation Planning for an Actual Machine Tool Based on Updatable Machining Database and Database-Oriented Planning Algorithm

Shinji Igari, Fumiki Tanaka, and Masahiko Onosato

Graduate School of Information Science and Technology, Hokkaido University, N14W9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan

Received:

April 20, 2012

Accepted:

July 7, 2012

Published:

November 5, 2012

Keywords:

operation planning, actual machine tool, machining database, updatable database, database-oriented algorithm

Abstract

To generate optimal machining information, a machining database and operation planning algorithm have to be customized for an actualmachine tool based on the knowledge of a skilled planner. In this paper, a method to update a machining database from NC data is proposed. The database is updated by adding machining information extracted fromNC data, based on tool path analysis. A method of generating a database-oriented planning algorithm from the machining database based on a decision tree method is also proposed. Machining strategy, cutting tool, and cutting conditions are determined based on the algorithm and k nearest neighbor method.

Cite this article as:

S. Igari, F. Tanaka, and M. Onosato, “Computer-Aided Operation Planning for an Actual Machine Tool Based on Updatable Machining Database and Database-Oriented Planning Algorithm,” Int. J. Automation Technol., Vol.6 No.6, pp. 717-723, 2012.

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References

[1] J. R. Quinlan, “C4.5: programs for machine learning,” Morgan Kaufmann Publishers, 1993.
[2] T. Segaran, “Programming Collective Intelligence,” O’Reilly Media, 2007
[3] H. Kodama, T. Hirogaki, E. Aoyama, and K. Ogawa, “Determination of end milling conditions using data mining – Combination effects of hierarchical and nonhierarchical clustering for tool catalog data,” J. of the Japan Society for Abrasive Technology, Vol.55, No.1, pp. 42-47, 2011. (in Japanese)
[4] X. Yan, K. Yamazaki, and J. Liu, “Extraction of milling know-how from NC programs through reverse engineering,” Int. J. of Production Research, Vol.38, No.11, pp. 2443-2457, 2000.
[5] X. Yan, K. Yamazaki, and J. Liu, “Reverse engineering of machining operation planning,” Int. J. of Production Research, Vol.39, No.9, pp. 1837-1849, 2001.
[6] Y. Uchida and H. Aoyama, “Development of Basic System to Construct Database of Machining Know-how,” The 4^th Int. Conf. on Leading Edge Manufacturing in 21^st Century, Proc., 2007.
[7] F. Tanaka, S. Igari, T. Kawaguchi, and M. Onosato, “Analysis of NC data based on feature information model of shape and process for retaining machining information,” Proc. of 43^rd CIRP Conf. on Manufacturing Systems, 2010.
[8] ISO 14649-1, “Industrial automation systems and integration – Physical device control – Data model for computerized numerical controllers – Part 1: Overview and fundamental principles,” 2003.
[9] ISO 6983-1, “Numeric control of machines – Program format and definition of address words – Part 1: Data format for positioning, line motion and contouring control systems,” 1982.
[10] X.W. Xu and Q. He, “Striving for a total integration of CAD, CAPP, CAM and CNC,” Robotics and Computer-Integrated manufacturing, Vol.20, No.2, pp. 101-109, 2004.
[11] X. Xu and J. Mao, “Development of the client tier for a STEP compliant CAPP system,” Proc. of the 6^th Int. Conf. on Frontiers of Design and Manufacturing, 2004.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J. R. Quinlan, “C4.5: programs for machine learning,” Morgan Kaufmann Publishers, 1993.

[2] [2] T. Segaran, “Programming Collective Intelligence,” O’Reilly Media, 2007

[3] [3] H. Kodama, T. Hirogaki, E. Aoyama, and K. Ogawa, “Determination of end milling conditions using data mining – Combination effects of hierarchical and nonhierarchical clustering for tool catalog data,” J. of the Japan Society for Abrasive Technology, Vol.55, No.1, pp. 42-47, 2011. (in Japanese)

[4] [4] X. Yan, K. Yamazaki, and J. Liu, “Extraction of milling know-how from NC programs through reverse engineering,” Int. J. of Production Research, Vol.38, No.11, pp. 2443-2457, 2000.

[5] [5] X. Yan, K. Yamazaki, and J. Liu, “Reverse engineering of machining operation planning,” Int. J. of Production Research, Vol.39, No.9, pp. 1837-1849, 2001.

[6] [6] Y. Uchida and H. Aoyama, “Development of Basic System to Construct Database of Machining Know-how,” The 4^th Int. Conf. on Leading Edge Manufacturing in 21^st Century, Proc., 2007.

[7] [7] F. Tanaka, S. Igari, T. Kawaguchi, and M. Onosato, “Analysis of NC data based on feature information model of shape and process for retaining machining information,” Proc. of 43^rd CIRP Conf. on Manufacturing Systems, 2010.

[8] [8] ISO 14649-1, “Industrial automation systems and integration – Physical device control – Data model for computerized numerical controllers – Part 1: Overview and fundamental principles,” 2003.

[9] [9] ISO 6983-1, “Numeric control of machines – Program format and definition of address words – Part 1: Data format for positioning, line motion and contouring control systems,” 1982.

[10] [10] X.W. Xu and Q. He, “Striving for a total integration of CAD, CAPP, CAM and CNC,” Robotics and Computer-Integrated manufacturing, Vol.20, No.2, pp. 101-109, 2004.

[11] [11] X. Xu and J. Mao, “Development of the client tier for a STEP compliant CAPP system,” Proc. of the 6^th Int. Conf. on Frontiers of Design and Manufacturing, 2004.