An in-line digital filtering algorithm for surface roughness profiles

Hara, Seiichiro; Tsukada, Tadao; Sasajima, Kazuyuki

doi:10.1016/s0141-6359(98)00013-0

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…Therefore, a Gaussian filter was established. Implementations of this filter were presented in [ 9 , 10 , 11 , 13 ]. Then, a Gaussian regression filter, working without running-in and running-out lengths was established.…”

Section: Measurement and Characterization Of Two-process Random Surfacesmentioning

confidence: 99%

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Two-Process Random Textures: Measurement, Characterization, Modeling and Tribological Impact: A Review

2021

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Two-process random textures seem to present better functional properties than one-process surfaces. There are many random two-process textures. Plateau-honed cylinder surfaces are the most popular example. Two-process surfaces are also created during the initial periods of life of machined elements. However, knowledge about two-process textures measurement, modeling, and behavior is low. Two-process surfaces are very sensitive to measurement errors. It is very difficult to model them. Special methods of their characterization were created. Their functional significance was studied in a small number of publications. In this paper, measurement, characterization, and modeling of two-process textures were presented. The functional impact of them was analyzed, the effects on contact mechanics and friction and wear were mainly studied. Finally, considerations of future challenges were addressed. The nature of two-process random textures should be taken into account during analyses of properties of machined elements. The plateau part decides about the asperity contact, and the valley portion governs the hydrodynamic lubrication.

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Section: Measurement and Characterization Of Two-process Random Surfacesmentioning

confidence: 99%

“…Initial methods of analysis, such as filtering, using Gaussian filters [ 8 , 9 , 10 , 11 , 12 , 13 ] of textures, were related to surfaces of symmetrical ordinate distribution. However, two-process surfaces and generally multiprocess textures seem to present better functional properties than one-process surfaces.…”

Section: Introductionmentioning

confidence: 99%

Two-Process Random Textures: Measurement, Characterization, Modeling and Tribological Impact: A Review

2021

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“…In 1996, Krystek proposed a fast and reliable convolution algorithm based on the recursive relationship of weighting functions that speeds up the extraction of surface roughness by Gaussian filters [14]. In 1998, Hara et al proposed an online Gaussian filtering algorithm that not only improves the computational efficiency, but also saves storage space and is very suitable for real-time surface roughness measurement system [15]. In 2000, Yuan et al used the sampling function (sin u/u) n (n → ∞) to approximate the Gaussian weight function.…”

Section: Gaussian Profile 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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“…Recently, Gaussian filtering technique was adopted to filtration of surface profiles. The authors of References [2,3,4,5] presented procedures of Gaussian filter implementation. This kind of filter was also recommended in 3D system [6,7].…”

Section: Introductionmentioning

confidence: 99%