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

Research output: Contribution to journalArticlepeer-review

66 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)95-100
Number of pages6
JournalPrecision Engineering
Volume29
Issue number1
DOIs
StatePublished - 1 Jan 2005

Keywords

  • Adaptive neuro-fuzzy inference system (ANFIS)
  • Computer vision
  • Fuzzy neural network
  • Modeling
  • Polynomial network
  • Surface roughness

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