Paper Title:
Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations
  Abstract

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

  Info
Periodical
Advanced Materials Research (Volumes 383-390)
Chapter
Chapter 5: Computer-Aided Design in Materials Engineering
Edited by
Wu Fan
Pages
1062-1070
DOI
10.4028/www.scientific.net/AMR.383-390.1062
Citation
A. H. Suhail, N. Ismail, S.V. Wong, N.A. Abdul Jalil, "Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations", Advanced Materials Research, Vols. 383-390, pp. 1062-1070, 2012
Online since
November 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Ying Xue Yao, Chang Qing Liu, Jian Guang Li, H.J. Jing, S.D. Chen
Abstract:Traditional adaptive control technologies in machining process optimization are limited in applications because they depend much on sensors,...
1
Authors: S. Jiang, Yan Shen Xu, J. Wu
Abstract:To improve the cutting efficiency, one of key approaches is to control with constant force in the full depth working condition. And the...
85
Authors: Yuan Wei Wang, Song Zhang, Jian Feng Li, Tong Chao Ding
Abstract:In this paper, Taguchi method was applied to design the cutting experiments when end milling Inconel 718 with the TiAlN-TiN coated carbide...
911
Authors: Pedro Jose Arrazola, A. Villar, R. Fernández, J. Aperribay
Abstract:This article describes a practical machining training aiming that the students acquire the theoretical-practical knowledge of chip formation...
83
Authors: Rao T. Sadasiva, K. Satyanarayana, Y. Praneeth, Anne Venu Gopal
Chapter 15: Meso/Micro Manufacturing Equipment and Processes
Abstract:Milling is the most widely applied machining process for producing flat surfaces and prismatic shapes. To minimize the process time and...
3147