Title: Quality improvement of machine vision-based non-contact inspection of surface roughness in turning through adaptive neuro-fuzzy interference system

Authors: D. Shome, P.K. Ray, B. Mahanty

Addresses: Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur – 721302, India. ' Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur – 721302, India. ' Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur – 721302, India

Abstract: Today|s competitive global manufacturing scenario drives a strong demand for accurate non-contact automated measurement of surface roughness in turning operations. This paper investigates the potential of various possible combinations of a number of surface image features, such as, statistical features extracted from grey-level co-occurrence matrices, discrete cosine transform coefficient and discrete Fourier transform coefficient, in machine vision-based non-contact measurement of surface roughness of turned AISI1045 steel work pieces. Adaptive neuro-fuzzy interference system (ANFIS) models, each of which utilises a particular combination of the above-mentioned image features for accomplishing non-contact prediction of surface roughness, are developed and compared in this paper. Analyses of experimental data demonstrate that the approach, which utilises statistical features for non-contact measurement of surface roughness, outperforms the other approaches in terms of surface roughness prediction accuracy and yields substantial improvement in the accuracy level of machine vision-based non-contact measurement of surface roughness in turning.

Keywords: machine vision; surface roughness; adaptive neuro-fuzzy interference system; ANFIS; statistical features; discrete cosine transform; DCT; discrete Fourier transform; DFT; non-contact inspection; turning; quality improvement; steel machining; neural networks; fuzzy logic.

DOI: 10.1504/IJPQM.2009.023700

International Journal of Productivity and Quality Management, 2009 Vol.4 No.3, pp.324 - 344

Available online: 08 Mar 2009 *

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