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Optimum Bar-feeder Support Positions of a Miniature High Speed Spindle System by Genetic Algorithm  

Lee, Jae-Hoon (School of Mechanical Engineering, Pusan National Univ.)
Kim, Mu-Su (School of Mechanical Engineering, Pusan National Univ.)
Park, Seong-Hun (School of Mechanical Engineering, Pusan National Univ.)
Kang, Jae-Keun (R&D, Hanwha TechM)
Lee, Shi-Bok (School of Mechanical Engineering, Pusan National Univ.)
Publication Information
Journal of the Korean Society for Precision Engineering / v.26, no.11, 2009 , pp. 99-107 More about this Journal
Abstract
Since a long work piece influences the natural frequency of the entire system with a miniature high speed spindle, a bar-feeder is used for a long work piece to improve the vibration characteristics of a spindle system. Therefore, it is very important to design optimally support positions between a bar-feeder and a long work piece for a miniature high speed spindle system. The goal of the current paper is to present an optimization method for the design of support positions between a bar-feeder and a long work piece. This optimization method is effectively composed of the method of design of experiment (DOE), the artificial neural network (ANN) and the genetic algorithm (GA). First, finite element models which include a high speed spindle, a long work piece and the support conditions of a bar-feeder were generated from the orthogonal array of the DOE method, and then the results of natural vibration analysis using FEM were provided for the learning inputs of the neural network. Finally, the design of bar-feeder support positions was optimized by the genetic algorithm method using the neural network approximations.
Keywords
High Speed Machining Center; Spindle System; Long Work-piece; Bar-feeder Support Positions; Design of Experiment; Artificial Neural Network; Genetic Algorithm; Optimal Design;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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