NDLI: Neural Network-based Dynamic Planning Model for Process Parameter Determination

Content Provider	IEEE Xplore Digital Library
Author	Jaekoo Joo
Copyright Year	2006
Description	Author affiliation: Dept. of Syst. & Manage. Eng., Inje Univ., Kyungnahm (Jaekoo Joo)
Abstract	Determining such process parameters as rotational speed, feed rate, depth of cut, and width of cut is the critical function that affects not only machining productivity but also quality of a finished part. In the paper, a dynamic planning model is developed to determine efficient process parameters for roughly machining a pocket type of process feature in the shop floor. A neural network structure is proposed to implement the dynamic planning model. The learning patterns used for training the neural network are acquired through simulation-based optimization procedures. The procedure finds an optimal set of process parameters for each set of operating factors by considering the machining costs, cutting forces, and machining power. A prototype system is developed and experimented to demonstrate the feasibility of the proposed model. Due to the dynamic planning model approach, the fatal weaknesses of conventional processing parameter determination can be conquered by its efficient, dynamic, and adaptive planning ability
Sponsorship	IEEE Comput Intelligence Soc. Eur. Soc. for Fuzzy Logic and Technol. Eur. Neural Networks Soc. Int. Assoc. for Fuzzy Set in Manage. and Economy Japan Soc. for Fuzzy Theor. and Soc. for Fuzzy Theor. and Intelligent Informatics Taiwan Fuzzy Syst. Assoc. World Wide Web Bus. Intelligence Hungarian Fuzzy Assoc. Univ. of Canberra
Starting Page	117
Ending Page	122
File Size	240783
Page Count	6
File Format	PDF
ISBN	0769525040
DOI	10.1109/CIMCA.2005.1631455
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-28
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Productivity Neural networks Process planning Fixtures Prototypes Machining Power system planning Cost function Feeds Power system modeling
Content Type	Text
Resource Type	Article

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