Computation of surface roughness using optical correlation

Hamed, A. A.; Saudy, M. A.

doi:10.1007/s12043-007-0081-x

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…It was assumed that the values of roughness parameters increase asymptotically with the increasing profile length [81]. The profile line shapes of the ACF intensities are obtained at different heights, and it is shown that the shapes are affected by noise [82], depending on the frequency type of noise [83]. By studying the shape of the ACF, the detection and reduction in selected types (frequencies) of measurement errors (noise) can be significantly improved [84].…”

Section: Applied Methodsmentioning

confidence: 99%

Reduction in Errors in Roughness Evaluation with an Accurate Definition of the S-L Surface

et al. 2023

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Characterization of surface topography, roughly divided into measurement and data analysis, can be valuable in the process of validation of the tribological performance of machined parts. Surface topography, especially the roughness, can respond straightly to the machining process and, in some cases, is defined as a fingerprint of the manufacturing. When considering the high precision of surface topography studies, the definition of both S-surface and L-surface can drive many errors that influence the analysis of the accuracy of the manufacturing process. Even if precise measuring equipment (device and method) is provided but received data are processed erroneously, the precision is still lost. From that matter, the precise definition of the S-L surface can be valuable in the roughness evaluation allowing a reduction in the rejection of properly made parts. In this paper, it was proposed how to select an appropriate procedure for the removal of the L- and S- components from the raw measured data. Various types of surface topographies were considered, e.g., plateau-honed (some with burnished oil pockets), turned, milled, ground, laser-textured, ceramic, composite, and, generally, isotropic. They were measured with different (stylus and optical) methods, respectively, and parameters from the ISO 25178 standard were also taken into consideration. It was found that commonly used and available commercial software methods can be valuable and especially helpful in the precise definition of the S-L surface; respectively, its usage requires an appropriate response (knowledge) from the users.

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Section: Applied Methodsmentioning

confidence: 99%

Reduction in Errors in Roughness Evaluation with an Accurate Definition of the S-L Surface

et al. 2023

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“…It is needed a translation of the surface or the detector to have an intensity of the speckle field high enough for the calculation. This real time technique has a range of measurement limited to less than 1 µm [ 19 ]; Digital holography: based on optical interference and diffraction. It uses holograms to obtain the intensity and the phase of an object [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Metrological Analysis of a Backlit Vision System for Surface Roughness Measurements of Turned Parts

Baleani

Paone

Gladines

et al. 2023

Sensors

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The focus of this study is to design a backlit vision instrument capable of measuring surface roughness and to discuss its metrological performance compared to traditional measurement instruments. The instrument is a non-contact high-magnification imaging system characterized by short inspection time which opens the perspective of in-line implementation. We combined the use of the modulation transfer function to evaluate the imaging conditions of an electrically tunable lens to obtain an optimally focused image. We prepared a set of turned steel samples with different roughness in the range Ra 2.4 µm to 15.1 µm. The layout of the instrument is presented, including a discussion on how optimal imaging conditions were obtained. The paper describes the comparison performed on measurements collected with the vision system designed in this work and state-of-the-art instruments. A comparison of the results of the backlit system depends on the values of surface roughness considered; while at larger values of roughness the offset increases, the results are compatible with the ones of the stylus at lower values of roughness. In fact, the error bands are superimposed by at least 58% based on the cases analyzed.

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“…One of the quality measures that is especially challenging for online examination is surface roughness of machined parts. Surface roughness is defined as an amplitude value measuring the vertical heights of the surface deviations from a reference line [1]. Inadequate surface roughness of machined parts can significantly affect the functionality of a product and can lead to a premature failure.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Design of a System for Online Surface Roughness Measurements

Koblar

Filipič

2021

Mathematics

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Surface roughness is one of the key characteristics of machined components as it affects the surface quality and, consequently, the lifetime of the components themselves. The most common method of measuring the surface roughness is contact profilometry. Although this method is still widely applied, it has several drawbacks, such as limited measurement speed, sensitivity to vibrations, and requirement for precise positioning of the measured samples. In this paper, machine vision, machine learning and evolutionary optimization algorithms are used to induce a model for predicting the surface roughness of automotive components. Based on the attributes extracted by a machine vision algorithm, a machine learning algorithm generates the roughness predictive model. In addition, an evolutionary algorithm is used to tune the machine vision and machine learning algorithm parameters in order to find the most accurate predictive model. The developed methodology is comparable to the existing contact measurement method with respect to accuracy, but advantageous in that it is capable of predicting the surface roughness online and in real time.

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Computation of surface roughness using optical correlation

Cited by 11 publications

References 32 publications

Reduction in Errors in Roughness Evaluation with an Accurate Definition of the S-L Surface

Reduction in Errors in Roughness Evaluation with an Accurate Definition of the S-L Surface

Design and Metrological Analysis of a Backlit Vision System for Surface Roughness Measurements of Turned Parts

Evolutionary Design of a System for Online Surface Roughness Measurements

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