Non-contact estimation of surface roughness in turning using computer vision and Artificial Neural Networks

D. Shome, P.K. Ray and B. Mahanty

International Journal of Industrial and Systems Engineering, 2009, vol. 4, issue 4, 349-367

Abstract: Accurate non-contact estimation of surface roughness in turning operations plays an important role in the manufacturing industries. This paper investigates the effectiveness of using various surface image features, such as contrast, energy, homogeneity, entropy, range, and standard deviation for computer vision-based non-contact estimation of surface roughness in turning operations. A Bayesian Regularisation-aided Artificial Neural Network (ANN) model-based approach is proposed in this paper for accomplishing surface roughness estimation. Analyses of experimental data demonstrate that the proposed approach yields significant improvement in the accuracy level of computer vision-based non-contact estimation of surface roughness (without involving turning parameters) of turned workpieces.

Keywords: computer vision; non-contact surface roughness estimation; turning; GLCM; grey-level co-occurrence matrix; ANNs; artificial neural networks; Bayesian regularisation; image textural features. (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:4:y:2009:i:4:p:349-367

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