Method for establishing contour baseline in twodimension surface roughness assessment
Method for establishing contour baseline in twodimension surface roughness assessment
 CN 101,082,484 A
 Filed: 06/25/2007
 Published: 12/05/2007
 Est. Priority Date: 06/25/2007
 Status: Active Application
First Claim
1. , set up the method for profile datum line in a kind of twodimensional surface roughness assessment, it is characterized in that step is as follows:
 (1) gathers or from data file, is written into surface profile data, according to waiting that evaluating surperficial actual state chooses sample length l and evaluation length l _{n}(2) according to selected sample length l and evaluation length l _{n}, the intercepting outline data is;
x ^{(0)}＝
{x ^{(0)}(1)，
x ^{(0)}(2)，
…
，
x ^{(0)}(N)} (1) Wherein, N is the number of original contour sampled data in the evaluation length, and establishing the interior sampled data number of a sample length is n, with sequence x ^{(0)}In from x ^{(0)}(m+1) Kai Shi continuous n item is as the m of original contour sampled data sequence constantly, that is, x _{m}^{(0)}＝
{x _{m}^{(0)}(1)，
x _{m}^{(0)}(2)，
…
，
x _{m}^{(0)}(n)}＝
{x ^{(0)}(m+1)，
x ^{(0)}(m+2)，
…
，
x ^{(0)}(m+n)} (2) Wherein, m=0,1,2 ..., Nn;
(3) utilize the grey roll modeling, to the m moment sequence x of original contour sampled data _{m}^{(0)}Carry out grey modeling, obtain the gray model value sequence;
${{\hat{x}}_{m}}^{\left(0\right)}(k+1)={{\hat{x}}_{m}}^{\left(1\right)}(k+1){{\hat{x}}_{m}}^{\left(1\right)}\left(k\right);$(3) (4) sequence x is obtained in the gray model value sequence set that is obtained by step (3) _{m}^{(0)}Pairing gray model curve;
(5) all model curves that step (4) is obtained comprehensively obtain a smooth contour curve, and this curve is the profile datum line of roughness assessment.
Chinese PRB Reexamination
Abstract
There is a sort of method which establishes the contour baseline in the assessment of the twodimension surface roughness, it is characterized in that: it uses gray rolling model to progress the gray modeling to a sampled data which is in the original contour of the sampling length, thereby obtain the model curve of the contour. And make the corresponding model curve of the sampled data which is in the assessing length to progress colligation, thereby obtain a slick curve of the contour, and make this curve to become the contour baseline which is in the assessment of the roughness. The original data of the contour does not obey the classical distribution, and in the assessing course it does not loss original data. It obtains the gray baseline in the whole assessing length, and does not wipe off the error of the shape in advance, the gray is slick and natural in the whole assessing length, and it is close to the Gauss baseline.

10 Citations
Measurement and evaluation method for roughness of meshing contact surface of cylindrical gear  
Patent #
CN 111,288,936 A
Filed 03/03/2020

Current Assignee

A kind of titanium alloy turnery processing 3 d surface topography analysis method  
Patent #
CN 109,855,593 A
Filed 03/12/2019

Current Assignee

Method is determined based on the structural plane threedimensional roughness coefficient of progressive sampling  
Patent #
CN 109,443,256 A
Filed 11/07/2018

Current Assignee

Method for extracting image characteristics by multivariate gray modelbased bidimensional empirical mode decomposition  
Patent #
CN 102,542,296 A
Filed 01/10/2012

Current Assignee

Diesel engine fault prediction method based on gray model  
Patent #
CN 102,705,078 A
Filed 04/19/2012

Current Assignee

Wavelet method for recognizing fractal feature length scale parameters on machined surface profile  
Patent #
CN 105,631,120 A
Filed 12/28/2015

Current Assignee

A kind of method using gear measuring center Measurement and evaluation toothface roughness  
Patent #
CN 108,007,326 A
Filed 12/04/2017

Current Assignee

Texture classification apparatus employing coarseness and directiviety of patterns  
Patent #
CN 1,164,716 A
Filed 10/28/1996

Current Assignee

Determination of roughness coefficient of rock mass structural face  
Patent #
CN 1,779,414 A
Filed 11/18/2004

Current Assignee

Image processing apparatus, image processing method, and computer program product  
Patent #
US 20070127837A1
Filed 12/06/2006

Current Assignee
N/A

2 Claims

1. , set up the method for profile datum line in a kind of twodimensional surface roughness assessment, it is characterized in that step is as follows:

(1) gathers or from data file, is written into surface profile data, according to waiting that evaluating surperficial actual state chooses sample length l and evaluation length l _{n} (2) according to selected sample length l and evaluation length l _{n}, the intercepting outline data is;
x ^{(0)}＝
{x ^{(0)}(1)，
x ^{(0)}(2)，
…
，
x ^{(0)}(N)} (1)Wherein, N is the number of original contour sampled data in the evaluation length, and establishing the interior sampled data number of a sample length is n, with sequence x ^{(0)}In from x ^{(0)}(m+1) Kai Shi continuous n item is as the m of original contour sampled data sequence constantly, that is, x _{m}^{(0)}＝
{x _{m}^{(0)}(1)，
x _{m}^{(0)}(2)，
…
，
x _{m}^{(0)}(n)}＝
{x ^{(0)}(m+1)，
x ^{(0)}(m+2)，
…
，
x ^{(0)}(m+n)} (2)Wherein, m=0,1,2 ..., Nn; (3) utilize the grey roll modeling, to the m moment sequence x of original contour sampled data _{m}^{(0)}Carry out grey modeling, obtain the gray model value sequence;
${{\hat{x}}_{m}}^{\left(0\right)}(k+1)={{\hat{x}}_{m}}^{\left(1\right)}(k+1){{\hat{x}}_{m}}^{\left(1\right)}\left(k\right);$ (3)(4) sequence x is obtained in the gray model value sequence set that is obtained by step (3) _{m}^{(0)}Pairing gray model curve;
(5) all model curves that step (4) is obtained comprehensively obtain a smooth contour curve, and this curve is the profile datum line of roughness assessment.


2. set up the method for profile datum line in the twodimensional surface roughness assessment according to claim 1, it is characterized in that:
 the grey modeling method of described step (3) is;
(1) to sequence x _{m}^{(0)}Carry out oneaccumulate and generate, obtain formation sequence x _{m}^{(1)};
x _{m}^{(1)}＝
{x _{m}^{(1)}(1)，
x _{m}^{(1)}(2)，
…
，
x _{m}^{(1)}(n)}Wherein ${{x}_{m}}^{\left(1\right)}\left(k\right)=\underset{}{\overset{}{\mathrm{\Σi=1k{{x}_{m}}^{\left(0\right)}\left(i\right),}}}$ k＝
1，
2，
…
，
n。
It is close to equal value sequence;
z _{m}^{(1)}={ z _{m}^{(1)}(1), z _{m}^{(1)}(2) ..., z _{m}^{(1)}(n) }Wherein, ${{z}_{m}}^{\left(1\right)}\left(k\right)=\left\{\begin{array}{cc}{{x}_{m}}^{\left(1\right)}\left(1\right),& k=1\\ \frac{1}{2}({{x}_{m}}^{\left(1\right)}\left(k\right)+{{x}_{m}}^{\left(1\right)}(k1)),& k=\mathrm{2,3},\·CenterDot;CenterDot;,n;\end{array}\right.$ (2) set up the single order grey differential equation of formation sequence; x _{m}^{(0)}(k)+a _{m}z _{m}^{(1)}(k)＝
b _{m}Wherein, a _{m}And b _{m}Be the undetermined parameter of the grey differential equation, the abovementioned single order grey differential equation be expressed as with matrix form;
Y _{m}＝
φ
_{m}θ
_{m}Wherein, ${Y}_{m}=\left(\begin{array}{c}{{x}_{m}}^{\left(0\right)}\left(2\right)\\ {{x}_{m}}^{\left(0\right)}\left(3\right)\\ \·\end{array}\right)$
CenterDot;&
CenterDot;x m ( 0 ) ( n ) , ${\mathrm{\φm=\left(\begin{array}{cc}{{z}_{m}}^{\left(1\right)}\left(2\right)& 1\\ {{z}_{m}}^{\left(1\right)}\left(3\right)& 1\\ CenterDot;\end{array}\right)}}_{}$
CenterDot;1 &
CenterDot; z m ( 1 ) ( n ) 1 , ${\mathrm{\θm=\left(\begin{array}{c}{a}_{m}\\ {b}_{m}\end{array}\right)}}_{}$ Following formula is n1 dimension binary inconsistent equation group, wherein Y _{m}And φ
_{m}Be known quantity, θ
_{m}Be undetermined parameter.Find the solution this inconsistent equation group, can get θ
_{m}The leastsquares estimation value;
 the grey modeling method of described step (3) is;
Specification(s)