Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system

Kuang Chyi Lee*, Shinn Jang Ho, Shinn-Ying Ho

*Corresponding author for this work

研究成果: Article同行評審

67 引文 斯高帕斯(Scopus)

摘要

Accurate estimation of surface roughness of workpieces in turning operations play an important role in the manufacturing industry. This paper proposes a method using an adaptive neuro-fuzzy inference system (ANFIS) to establish the relationship between actual surface roughness and texture features of the surface image. The accurate modeling of surface roughness can effectively estimate surface roughness. The input parameters of a training model are spatial frequency, arithmetic mean value, and standard deviation of gray levels from the surface image, without involving cutting parameters (cutting speed, feed rate, and depth of cut). Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling and estimating surface roughness. Experimental results show that the proposed ANFIS-based method outperforms the existing polynomial-network-based method in terms of training and test accuracy of surface roughness.

原文English
頁(從 - 到)95-100
頁數6
期刊Precision Engineering
29
發行號1
DOIs
出版狀態Published - 1 一月 2005

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