Quality improvement of machine vision-based non-contact inspection of surface roughness in turning through adaptive neuro-fuzzy interference system. (11th March 2009)

• Record Type: Journal Article
• Authors: Shome, D.; Ray, P.K.; Mahanty, B.
• Is Part Of: International journal of productivity and quality management. Volume 4:Number 3(2009)
• Page Start: 324
• Page End: 344
• Publication Date: 2009-03-11
• Subjects: 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; Quality control -- Management -- Periodicals; Industrial productivity -- Management -- Periodicals; 658.5005
• Languages: English
• ISSNs: 1746-6474

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.