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Research Papers

Support Vector Fuzzy Adaptive Network in the Modeling of Material Removal Rate in Rotary Ultrasonic Machining

[+] Author and Article Information
Judong Shen, Z. J. Pei

Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, KS 66506

E. S. Lee1

Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, KS 66506eslee@ksu.edu

1

Corresponding author.

J. Manuf. Sci. Eng 130(4), 041005 (Jul 10, 2008) (8 pages) doi:10.1115/1.2951935 History: Received February 02, 2007; Revised April 14, 2008; Published July 10, 2008

Rotary ultrasonic machining (RUM) is one of the cost-effective machining methods for machining difficult to process material. It is a hybrid machining process that combines the material removal mechanisms of diamond grinding with ultrasonic machining. However, due to the lack of understanding of the mechanisms of these operations, models for these machining processes are difficult to establish. In this paper, the support vector fuzzy adaptive network (SVFAN), a parameter free nonlinear regression technique, is used to model the material removal rate in RUM. The SVFAN retains the advantages of both the fuzzy adaptive networks and the support vector machines. The former possesses the linguistic representation ability and the latter is a very effective learning machine. The results are compared with that obtained by the use of fuzzy adaptive network and it is shown that the combined approach is a more effective algorithm for the modeling of complex manufacturing processes.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 3

General architecture of FAN

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Figure 4

SVN for classification and regression

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Figure 5

Architecture of the proposed SVFAN

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Figure 6

Flow of SVFAN learning algorithm

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Figure 7

Convergence behavior of SVFAN

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Figure 8

Convergence behavior of FAN III

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Figure 1

Illustration of RUM

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Figure 2

Schematic illustration of the experimental setup

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