Identification of surface roughness parameters using wavelet transforms

Jana, Bhaskara Rao; Seventline, J. Beatrice

doi:10.1109/eesco.2015.7253777

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…In 2015, Jana et al used wavelet analysis to extract wavelength-based surface features from surface profiles. The surface roughness was separated from the actual profile, and the roughness value Ra was obtained [97]. In 2017, Wang et al used wavelet packet transform technology and a profiler to extract the areal roughness parameters of milling, turning, and grinding surfaces, respectively.…”

Section: International Standards Are Planning To Include Areal Filtersmentioning

confidence: 99%

Metrology and Measurement Systems

He,

Zheng,

Ding

et al. 2021

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Surface roughness is an important indicator in the evaluation of machining and product quality, as well as a direct factor affecting the performance of components. A rapidly developing filtering technology has become the main means of extracting surface roughness. The International Organization for Standardization (ISO) is constantly updating and improving the standard system for filtering technology in order to meet the requirements of technological development. Based on the filters already accepted by the international standard ISO 16610, this study briefly introduces the filtering principle of each filter, reviews the development of each filter in the application of surface roughness, and compares the advantages and limitations of their individual performances. The application range of each filter is summarized and, finally, the future direction of the digital filtering used in surface roughness is extrapolated.

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Section: International Standards Are Planning To Include Areal Filtersmentioning

confidence: 99%

Metrology and Measurement Systems

He,

Zheng,

Ding

et al. 2021

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“…The wavelet technique decomposes the images into various terms like estimation details, vertical details, horizontal details and the diagonal details of an image. By using multilevel wavelet, the input image is decayed into several frequency components and it can separate the high frequency components available there [7]. The spatial resolution can be improved by the available techniques.…”

Section: A Introductionmentioning

confidence: 99%

Wavelets and Artificial Neural Networks for Surface Roughness Estimation of Ceramic Tiles

jana,

Inakoti

et al. 2024

Preprint

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An important prerequisite for machined surfaces is the ability to estimate the surface roughness parameter. Non-contact methods are among the many measurement techniques that are frequently used to gauge the roughness of machined surfaces. These techniques are quick and adaptable. It is crucial in industries that produce ceramic tiles, glass, wood, and iron. This research proposes a novel method for measuring ceramic tile surfaces' surface roughness metrics through image processing. This system's acquired image is examined to see how its properties relate to those of the surface roughness. The energy details in terms of approximation, horizontal, vertical, and diagonal detail coefficients were derived after enhancement using a wavelet decomposition approach. The use of wavelet-based feature extraction in the evaluation of surface roughness was made justifiable by the energy details' strong correlation with the surface roughness parameter. Artificial neural networks (ANN) have been utilized to estimate Ra of the machined surfaces using the information collected from the wavelet transform of the pictures. Therefore, a correlation between image properties and Ra value has been attempted, effectively utilizing computer vision system for this application.

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Identification of surface roughness parameters using wavelet transforms

Cited by 2 publications

References 5 publications

Metrology and Measurement Systems

Metrology and Measurement Systems

Wavelets and Artificial Neural Networks for Surface Roughness Estimation of Ceramic Tiles

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