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Assembly Sequence Planning Using Inductive Learning
Takeshi Murayama*, Bungo Takemura** and Fuminori Oba*
*Department of Machine Design Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan
**Energy and Industrial Systems Center, Mitsubishi Electric Corporation, 6-14 Maruo-Machi, Nagasaki, 850-91, Japan
Received:April 1, 1999Accepted:April 2, 1999Published:August 20, 1999
Keywords:assembly planning, machine learning, CAD, CAPP
Abstract
The authors propose acquiring heuristic rules automatically for generating assembly sequences efficiently. Heuristic rules are reduced from training examples by inductive learning. Additional training examples are made from information on assembly sequences and used for modifying heuristic rules. As the assembly sequence generation and modification of heuristic rules are executed more, heuristic rules are refined and assembly sequences are generated efficiently. An experiment demonstrated the effectiveness of the approach.
Cite this article as:T. Murayama, B. Takemura, and F. Oba, “Assembly Sequence Planning Using Inductive Learning,” J. Robot. Mechatron., Vol.11 No.4, pp. 315-320, 1999.Data files:
Copyright© 1999 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.
