Application of image edge detection methods for precise estimation of the standard surface roughness parameters: Polypropylene/ethylene-propylene-diene-monomer blend as a case study

Abstract

Material surface roughness is an important property in different fields of science and its precise measurement is still a serious concern. Roughness measurement is commonly implemented using stylus profilometer. Although it suffers from some drawbacks such as low speed and destructive nature. Optical methods, such as machine vision coupled with image texture analysis have shown promising capability for noncontact/nondestructive roughness measurement. In the present study, a roughness evaluation method is proposed based on image edge detection algorithms. The method was applied to investigate the surface roughness of polypropylene/ethylene-propylene-diene-monomer (PP/EPDM) blend as an important engineering plastic. Different roughness patterns were created on PP/EPDM sheets employing hot press processing. Images of the roughened samples were captured with 10 different resolutions. In the proposed method, the performance of five different edge detectors including Roberts, Prewitt, Sobel, Laplacian of Gaussian (LoG), and Canny were examined. The results showed that LoG method in the images with 200 dpi resolution effectively evaluates PP/EPDM surface roughness. Linear correlation coefficients (R2) between LoG results and Stylus profilometry results was greater than 0.98. Moreover, some mathematical models were developed for evaluation of the roughness parameters based on LoG edge frequency. The models’ results showed 6.7% deviation from stylus profilometry results in the worst case. The proposed method can be used as a feasible solution for roughness evaluation of polymeric materials in online inspections.