ISO 16610-71:2014 pdf download.Geometrical product specifications (GPS) — Filtration — Part 71: Robust areal filters: Gaussian regression filters
This part of ISO 16610 specifies the characteristics of the robust areal Gaussian regression filter for the evaluation of surfaces that may contain spike discontinuities as well as deep valleys and high peaks. It specifies in particular how to separate large scale lateral components and short scale lateral components of a surface.
2 Normative references
The following documents, in whole or in part, are normatively referenced in this document and are indispensable for its application. For dated references, only the edition cited applies. For undated references, the latest edition of the referenced document (including any amendments) applies.
ISO 16610-1, Geometrical product specifications (GPS) — Filtration — Part 1: Overview and basic concepts ISO 16610-30:— 1) , Geometrical product specifications (GPS) — Filtration — Part 30: Robust profile filters: Basic concepts ISO/IEC Guide 99, International vocabulary of metrology — Basic and general concepts and associated terms (VIM) ISO/IEC Guide 98-3, Uncertainty of measurement — Part 3: Guide to the expression of uncertainty in measurement (GUM:1995)
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 16610-1, ISO 16610-30, ISO/IEC Guide 99 and ISO/IEC Guide 98-3 and the following apply.
3.1 robust planar filter non linear areal filter to separate a planar surface with specific phenomena (e.g. spike discontinuities as well as deep valleys and high peaks etc.) into large scale lateral components and short scale lateral components
3.2 robust cylindrical filter non linear areal filter to separate a cylindrical surface with specific phenomena (e.g. spike discontinuities as well as deep valleys and high peaks etc.) into large scale lateral components and short scale lateral components
3.4 robust areal regression filter
weighted M–estimator based on the areal local complete polynomial modelling of the surface Note 1 to entry: See ISO 16610-30 for the definition of the weighted M-estimator.
Note 2 to entry: See Annex A for the mathematical definition of the robust areal regression filter.
3.5 robust areal Gaussian regression filter
robust areal regression filter based on the areal Gaussian weighting function, the biweight influence function and a local complete polynomial modelling of the surface with the degree p =2 as the default case
Note 1 to entry: to entry:See ISO 16610-61 for the definition of the areal Gaussian weighting function.
Note 2 to entry: to entry:In case of p =2 , the robust areal Gaussian regression filter follows a complete polynomial up to second degree.
4 Robust planar Gaussian regression filter
4.1 General
Robust planar Gaussian regression filters complying to this document shall conform to sections 4.2 to
m number of the surface values in x direction
n number of the surface values in y direction
i index of the surface values in x direction i = 1,…,m
j index of the surface values in y direction j = 1,…,n
z ij surface values before filtering
w ij filtered surface values
λ c cut-off wavelength
Δ x sampling interval in x direction
Δ y sampling interval in y direction
NOTE 1 See ISO 16610-30 for the definition of Δ MAD .
NOTE 3 The definition for the value c is equivalent to 3 σ of a surface with a Gaussian amplitude distribution.
NOTE 4 The number of zeros in Formula (2) is equal to p p+ ( ) 3 2 (see Annex A).
NOTE 5 The values w ij are generally calculated by iteration starting with δ ij
1 = and updating the weights
according to Formula (10). For the calculation of the first updated weights δ ij, the default scale parameter c can
be increased by a factor of two.
4.4 Transmission characteristics
The weighting function of the robust planar Gaussian regression filter depends on the surface values and the location on the surface. Therefore no transmission characteristic can be given.
5 Robust cylindrical Gaussian regression filter
5.1 General
Robust cylindrical Gaussian regression filters complying to this document shall conform to sections 5.2 to 5.4.
5.2 Weighting function
The weighting function of the robust cylindrical Gaussian regression filter depends on the surface values (height to the reference surface) and the location of the weighting function on the surface.
5.4 Transmission characteristics
The weighting function of the robust cylindrical Gaussian regression filter depends on the surface values and the location on the surface. Therefore no transmission characteristic can be given.ISO 16610-71 pdf download.ISO 16610-71-2014 pdf download
