Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm-Reference-Cited by-同舟云学术

Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm

Published:2009-03 Issue:2 Volume:36 Page:3216-3222
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Ho Wen-Hsien,Tsai Jinn-Tsong,Lin Bor-Tsuen,Chou Jyh-Horng

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference17 articles.

1. Computer-aided analysis of a surface roughness model for end milling;Alauddin;Journal of Materials Processing Technology,1995

2. Prediction of tool life in end milling by response surface methodology;Alauddin;Journal of Materials Processing Technology,1997

3. Determination of the chip geometry, cutting force and roughness in free form surface finishing milling, with ball end tools;Bouzakis;International Journal of Machine Tools and Manufacture,2003

4. Integrated genetic programming and genetic algorithm approach to predict surface roughness;Brezocnik;Materials and Manufacturing Processes,2003

5. Prediction of surface roughness with genetic programming;Brezocnik;Journal of Materials Processing Technology,2004

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