CN113532275B - Non-contact R-test sphere center coordinate calibration method adopting laser displacement sensor - Google Patents

Non-contact R-test sphere center coordinate calibration method adopting laser displacement sensor Download PDF

Info

Publication number
CN113532275B
CN113532275B CN202110841893.2A CN202110841893A CN113532275B CN 113532275 B CN113532275 B CN 113532275B CN 202110841893 A CN202110841893 A CN 202110841893A CN 113532275 B CN113532275 B CN 113532275B
Authority
CN
China
Prior art keywords
test
formula
error
errors
laser displacement
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110841893.2A
Other languages
Chinese (zh)
Other versions
CN113532275A (en
Inventor
吕盾
张佳辉
陈银辉
郭俊康
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian Jiaotong University
Original Assignee
Xian Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian Jiaotong University filed Critical Xian Jiaotong University
Priority to CN202110841893.2A priority Critical patent/CN113532275B/en
Publication of CN113532275A publication Critical patent/CN113532275A/en
Application granted granted Critical
Publication of CN113532275B publication Critical patent/CN113532275B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/002—Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness

Abstract

A non-contact R-test sphere center coordinate calibration method adopting a laser displacement sensor firstly sets measuring points and collects experimental data; calibrating the conversion matrix, and preliminarily solving the coordinates of the point sphere center; then based on Gaussian regression, establishing a primary GPR error model which takes displacement data of three laser displacement sensors as input variables and takes errors of each position point in the directions of an X axis, a Y axis and a Z axis as dependent variables, and further solving error residual quantity; removing a plurality of points with space errors larger than a critical value, selecting a Matern2/5 kernel function to carry out secondary Gaussian process regression, establishing a secondary GPR position error calculation model of the data of the laser displacement sensor and the position point coordinate errors, and further obtaining the coordinate errors of all the position points; then, calculating the sphere center coordinate of the non-contact R-test; finally, the calculated sphere center coordinates are used in the calculation process of the machine tool key errors of the RTCP parameters and the geometric error thermal errors of the five-axis machine tool; the invention realizes accurate and efficient calculation of the sphere center coordinate.

Description

Non-contact R-test sphere center coordinate calibration method adopting laser displacement sensor
Technical Field
The invention belongs to the technical field of numerical control machines, and particularly relates to a non-contact R-test sphere center coordinate calibration method adopting a laser displacement sensor.
Technical Field
With the increase of the requirement of the machine tool for machining precision, the measurement of main key errors of the machine tool, such as geometric errors, dynamic errors and thermal errors in the machining process, is increasingly important, and the compensation of the main key errors by using the measurement result is an effective method for improving the machining precision of parts. An R-test tester is developed by S.Weikert of the Federal rational in Switzerland in 2004, can measure the position of a tool nose in three directions in a workpiece coordinate system, and has rich and practical application. In recent years, along with research and development of scholars, the R-test tester has been well applied to machine tool error measurement such as geometric errors and thermal errors.
The rapid and accurate calculation of the coordinates of the sphere center is a key problem of R-test measurement, and the R-test is divided into a contact type R-test and a non-contact type R-test according to different sensor types of the R-test. Compared with a contact type R-test, the non-contact type R-test avoids abrasion caused by contact friction and can realize measurement under a high-speed rotation condition; meanwhile, the non-contact R-test has higher measurement precision and higher sampling frequency, can realize the acquisition of the high-frequency displacement signals of the cutter point, and has higher sensitivity. However, for the calculation of the sphere center of the non-contact R-test, due to the influence of the curvature of the spherical surface of the detection sphere, the data change of the sensor and the displacement of the detection sphere are in a nonlinear relationship, so that a coordinate conversion method of a calibration conversion matrix of the contact R-test cannot be used, and the calculation method is complex and not perfect.
In the prior art, chinese patent (CN 109032069 a) discloses a non-contact R-test measuring instrument using an eddy current displacement sensor, which calculates the center coordinates by using a differential evolution algorithm according to an induced voltage characteristic curve equation of the eddy current displacement sensor and a sensor induced plane equation, and solves the coordinate result of an accurate center point in a measurement coordinate system.
Hong and Ibaraki compare four laser displacement sensors with different measurement principles in an article Non-contact R-test with laser displacement sensors for error calibration of five-axis machine tools, and provide a coordinate calculation method based on target sphere curvature compensation.
In conclusion, the prior art has the defect of low calculation efficiency, and the calculation accuracy needs to be further improved. At present, a non-contact R-test sphere center coordinate calculation method which adopts a laser displacement sensor and is efficient, accurate, simple and convenient is urgently needed.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a non-contact R-test sphere center coordinate calibration method adopting a laser displacement sensor, so that the sphere center coordinate can be accurately and efficiently calculated.
In order to achieve the purpose, the invention adopts the technical scheme that:
a non-contact R-test sphere center coordinate calibration method adopting a laser displacement sensor comprises the following steps:
1) setting measuring points and acquiring experimental data: setting calibration measuring points at certain intervals, and acquiring displacement data of each laser displacement sensor to obtain displacement values of three non-contact R-test laser displacement sensors;
2) calibrating a conversion matrix, and preliminarily solving the coordinates of the point sphere center: solving a conversion matrix T by referring to a contact type R-test sphere center calculation method based on the R-test sphere center theoretical position obtained in the step 1) and the displacement values of the three laser displacement sensorsLaserPreliminarily solving the sphere center coordinates of the measuring points by using the conversion matrix, and reducing the error of the sphere center coordinates to a micron level as shown in a formula (1);
OW＝PSen·TLaser (1)
in the formula OWFor the coordinate of the test point in the workpiece coordinate system, PSenReading by a laser displacement sensor;
3) based on Gaussian regression, establishing a primary GPR error model which takes displacement data of three laser displacement sensors as input variables and takes errors of each position point in the directions of an X axis, a Y axis and a Z axis as dependent variables, and further solving error residual quantity;
adopting a Gaussian process regression method in machine learning, selecting a kernel function as a rational quadratic kernel function, defining the Gaussian process as the formula (2),
f(x)～GP(m(x),k(x,x′)) (2)
where m (x) is a mean function and k (x, x') is a covariance function;
for the multiple nonlinear regression problem, the model is set as formula (3),
y＝f(x)+ε (3)
in the formula: x is the input vector; y is the output value; ε -noise;
assuming that the noise satisfies a random distribution, ε -N (0, σ)n 2) Obtaining prior distribution of output value y as formula (4), observed value y*And a predicted value f*The joint prior distribution of (a), as in equation (5),
Figure BDA0003179201940000031
Figure BDA0003179201940000032
in the formula: k (X, X) -an nxn order symmetric positive definite covariance matrix, K (X, X)*) -test point x*Covariance matrix of order n X1, K (X) with input X*,x*) -test point x*(ii) its own covariance; i isn-an identity matrix of order n,. sigman-standard deviation of the random distribution of noise;
calculate the predicted value f*The posterior distribution of (2) is shown in the formula (6), and the predicted value f*The mean and variance of (A) are respectively expressed by the formula (7) and the formula (8),
Figure BDA0003179201940000041
Figure BDA0003179201940000042
Figure BDA0003179201940000043
in the formula:
Figure BDA0003179201940000044
test point x*Corresponding predicted value f*The mean value of (a); cov (f)*)——f*The variance of (a);
based on the Gaussian Process Regression (GPR) method, data of three laser displacement sensors are used as input, error components of predicted point coordinates in the X direction, the Y direction and the Z direction are respectively used as output, and a GPR error model is generated;
selecting a rational quadratic kernel function as the kernel function of the Gaussian process regression, wherein the kernel function is in the form of formula (9),
Figure BDA0003179201940000045
4) removing a plurality of points with the spatial error larger than a critical value in the step 3), selecting a Matern2/5 kernel function to carry out secondary Gaussian process regression, establishing a secondary GPR position error calculation model of three laser displacement sensor data and position point coordinate errors, and further obtaining the coordinate error of each position point;
the quadratic GPR selects the Matern5/2 kernel function, which is shown in equation (10),
Figure BDA0003179201940000046
in the formula: ζ -smoothing coefficient; Gamma-Gamma function; h is a Bassel function, and when the smoothing coefficient is 0.5, the function is an exponential kernel; when the smoothing coefficient is infinite, the smoothing coefficient is a Gaussian kernel;
5) calculation of the center of sphere coordinates of the non-contact R-test: performing coordinate calculation by the transformation matrix in the step 2) to preliminarily obtain the center coordinates of each position point under the workpiece coordinate system, and compensating the preliminarily obtained center coordinates of each position point by calculating the center coordinates of each position point by the Gaussian regression model in the steps 3) and 4) twice to obtain accurate center coordinates of the non-contact R-test;
6) and (4) applying the sphere center coordinates calculated in the step 5) to the calculation process of the machine tool key errors of the RTCP parameters and the geometric error thermal errors of the five-axis machine tool.
When the dots are discarded in the step 4, the dots with large spatial errors are removed, the original data are kept as much as possible, and the critical value is selected to be 0.7 μm.
The invention has the following beneficial effects:
according to the non-contact R-test sphere center coordinate calibration method adopting the laser displacement sensor, the non-contact R-test sphere center coordinate can be accurately and efficiently calculated, and through verification, under a large sample (169416 x 3), compared with a neural network method, the method reduces the average space error from 0.303 mu m to 0.237 mu m, and improves the coordinate calculation precision by 21.8%; the calculation time is reduced from 6 minutes and 52 seconds to 4 seconds, and the calculation efficiency is improved by 99 percent; the method has important significance for development and use of the R-test and further detection and compensation of various errors in the whole machine tool industry.
Drawings
FIG. 1 is a calibration flow chart of the present invention.
FIG. 2 is a displacement diagram of three laser displacement sensors of a non-contact R-test according to an embodiment of the present invention.
FIG. 3 is a diagram of 729 coordinate prediction points and theoretical points in space according to an embodiment of the present invention.
FIG. 4 is a graph of error difference of 729 predicted points in the X-axis, Y-axis and Z-axis directions according to an embodiment of the present invention.
FIG. 5 is a diagram of residuals in three coordinate axes of GPR at a time in accordance with an embodiment of the present invention.
FIG. 6 is a diagram of a spatial error of a GPR predicted coordinate according to an embodiment of the present invention.
FIG. 7 is a diagram of residual errors in three coordinate axes of secondary GPR in accordance with an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
Referring to fig. 1, a non-contact R-test sphere center coordinate calibration method using a laser displacement sensor includes the following steps:
1) setting measuring points and acquiring experimental data: the measuring range of the adopted non-contact R-test laser displacement sensor is +/-1 mm, and the offset of the sphere center of the detection sphere in the direction of a single coordinate axis cannot reach +/-1 mm in a workpiece coordinate system; in order to ensure the safety of a machine tool, a detection ball and a laser displacement sensor, the offset of each direction of a workpiece coordinate system is set within the range of +/-0.4 mm; the calibrated measuring points are arranged at intervals of 0.1mm, and the total is 93The measuring points (namely 729) collect the displacement data of each laser displacement sensor to obtain three laser displacement transmissions of the non-contact R-testDisplacement values of the sensor, as shown in FIG. 2;
2) calibrating a conversion matrix, and preliminarily solving the coordinates of the center of a point sphere: based on the theoretical position of the center of the sphere of the R-test obtained in the step 1) and the displacement values of the three laser displacement sensors, obtaining the predicted coordinate values of 729 measuring points by referring to a coordinate calibration method of a contact type R-test and through a conversion matrix; firstly, extracting 27 measurement points from 729 space points, and obtaining a coordinate conversion matrix of a non-contact R-test by using R-test displacement and theoretical coordinates of the corresponding points, wherein the coordinate conversion matrix is shown in a formula (1);
Figure BDA0003179201940000061
using the transformation matrix T obtained by solvingLaserAccording to the formula (2), calculating the prediction coordinates of 729 measured points obtained by measurement;
OW＝PSen·TLaser (2)
in the formula OWFor the coordinates of the test point in the workpiece coordinate system, PSenReading by a laser displacement sensor;
the coordinates of 729 predicted points and the coordinates of the theoretical points are shown in FIG. 3, in which the coordinates of the predicted points and the theoretical points are approximately overlapped, but there is a certain degree of error; separating the position error of each coordinate point according to three directions of an X axis, a Y axis and a Z axis to obtain error components of each measuring point in three coordinate axis directions, as shown in FIG. 4;
3) based on Gaussian regression, establishing a primary GPR error model (error model for short) which takes displacement data of three laser displacement sensors as input variables and takes errors of each position point in the directions of an X axis, a Y axis and a Z axis as dependent variables, and further solving error residual quantity;
adopting a Gaussian process regression method in machine learning, selecting a kernel function as a rational quadratic kernel function, defining the Gaussian process as the formula (3),
f(x)～GP(m(x),k(x,x′)) (3)
where m (x) is a mean function and k (x, x') is a covariance function;
for the multiple nonlinear regression problem, the model is set as formula (4),
y＝f(x)+ε (4)
in the formula: x is the input vector; y is the output value; ε -noise;
assuming that the noise satisfies a random distribution, ε -N (0, σ)n 2) Obtaining the prior distribution of the output value y as a formula (5), and the joint prior distribution of the observed value y and the predicted value f as a formula (6),
Figure BDA0003179201940000071
Figure BDA0003179201940000072
in the formula: k (X, X) -an nxn order symmetric positive definite covariance matrix, K (X, X)*) -test point x*Covariance matrix of order n X1, K (X) with input X*,x*) -test point x*(ii) its own covariance; i isn-an identity matrix of order n,. sigmanStandard deviation of the random distribution of noise, K (X, X)*) And K (x)*X) have the same meaning;
calculate f*Has a posterior distribution of the formula (7), f*The mean and variance of (A) are respectively expressed by the formula (8) and the formula (9),
Figure BDA0003179201940000073
Figure BDA0003179201940000074
Figure BDA0003179201940000075
in the formula:
Figure BDA0003179201940000081
-test point x*Corresponding predicted value f*The mean value of (a); cov (f)*)——f*The variance of (a);
based on the Gaussian Process Regression (GPR) method, data of three laser displacement sensors are used as input, error components of predicted point coordinates in the X direction, the Y direction and the Z direction are respectively used as output, and a GPR error model is generated;
selecting a rational quadratic kernel function as a kernel function of Gaussian process regression, wherein the kernel function is in the form of a formula (10),
Figure BDA0003179201940000082
the displacement of three laser displacement sensors is input into a GPR error model to obtain error residuals, as shown in FIG. 5, the maximum value of the residuals exceeds 2 μm in the X direction, the residual of more than 10 points exceeds 1 μm, the maximum value of the residuals is close to 3 μm in the Y direction, the maximum value of the residuals exceeds 2 μm in the Z direction,
table 1 shows the information of the primary GPR error model of the rational quadratic kernel function, RMSE is the root mean square error, and the smaller the value, the closer the predicted result is to the true value; the R-square is the goodness of fit, the ideal value is 1, and the total time of one-time GPR error model training is about 15 s.
TABLE 1 GPR error model training information
Figure BDA0003179201940000083
4) Discarding a plurality of points with larger space errors after the first Gaussian regression, considering to remove the points with large errors and reserving original data as much as possible when discarding the points, selecting a critical value of 0.7 mu m, selecting a Matern5/2 kernel function to perform the second Gaussian regression, establishing a second GPR position error calculation model (coordinate error calculation model for short) between three displacement sensor data and position point coordinate errors, and further obtaining the position point coordinate errors;
the linear axis positioning error compensation is to realize accurate calibration of R-test, and needs to compensate positioning errors of three linear axes in advance, 729 points need to be positioned in the non-contact R-test calibration process, and the positioning accuracy of each linear axis is difficult to ensure, so that the positioning of the linear axes at some points is inaccurate, and the error of a few micrometers (the maximum is about 3 micrometers) exists; due to the influence of points with inaccurate positioning, the accuracy of a secondary GPR position error calculation model is reduced, so that a predicted point has a larger error with a theoretical point at some positions, and the accuracy and the effectiveness of a predicted result are difficult to ensure;
based on the reasons, on the basis of a primary GPR error model, the method uses GPR again for error calculation, and obtains the spatial errors corresponding to 729 measuring points and each point according to the primary GPR error model, as shown in FIG. 6;
selecting a point corresponding to data with an error value larger than a certain specific value (0.7 mu m in the text) as a point with a larger spatial error, and discarding the data of the point from the original laser displacement sensor displacement data to obtain a group of new laser displacement sensor displacement data, wherein compared with the original data, the data amount of the new data is reduced to 617 points, when the points are discarded, the points with the large error are removed, the original data is kept as much as possible, otherwise, the GPR accuracy of a secondary position error calculation model is influenced;
selecting a Matern5/2 kernel function to carry out secondary Gaussian regression, establishing a secondary GPR position error calculation model of three displacement sensor data and position point coordinate errors, selecting a Matern5/2 kernel function as the secondary GPR, wherein the kernel function is shown in a formula,
Figure BDA0003179201940000091
in the formula: ζ -smoothing coefficient; Gamma-Gamma function; h is a Bassel function, and when the smoothing coefficient is 0.5, the function is an exponential kernel; when the smoothing coefficient is infinite, the smoothing coefficient is a Gaussian kernel;
after regression (GPR) of a quadratic Gaussian process, residual errors in three coordinate axis directions are shown in FIG. 7, and the residual values are all smaller than 0.7 μm;
the information of the secondary GPR position error calculation model is shown in Table 2, compared with the information of the primary GPR, the RMSE values of the secondary GPR position error calculation model are all reduced, the R sides are all increased by 0.01, and the training time is basically kept unchanged and is about 5 s;
TABLE 2 quadratic GPR position error calculation model information
Figure BDA0003179201940000101
5) Calculating the sphere center coordinates of the non-contact R-test;
performing coordinate calculation by the transformation matrix in the step 2) to preliminarily obtain the center coordinates of each position point under the workpiece coordinate system, and compensating the preliminarily obtained center coordinates of each position point by calculating the center coordinates of each position point by the Gaussian regression model in the steps 3) and 4) twice to obtain accurate center coordinates of the non-contact R-test;
6) the sphere center coordinate calculated in the step 5) can be used in the calculation process of machine tool key errors such as RTCP parameters, geometric error thermal errors and the like of the five-axis machine tool, and has important significance for detection and compensation of various errors in the whole machine tool industry.

Claims (2)

1. A non-contact R-test sphere center coordinate calibration method adopting a laser displacement sensor is characterized by comprising the following steps:
1) setting measuring points and acquiring experimental data: setting calibration measuring points at certain intervals, and acquiring displacement data of each laser displacement sensor to obtain displacement values of three non-contact R-test laser displacement sensors;
2) calibrating a conversion matrix, and preliminarily solving the coordinates of the center of a point sphere: solving a conversion matrix T by referring to a contact type R-test sphere center calculation method based on the theoretical position of the R-test sphere center obtained in the step 1) and the displacement values of the three laser displacement sensorsLaserPreliminary solution using transformation matrixMeasuring the coordinates of the center of sphere of the point, and reducing the error of the coordinates of the center of sphere to micron level as shown in formula (1);
OW＝PSen·TLaser (1)
in the formula OWFor the coordinate of the test point in the workpiece coordinate system, PSenReading by a laser displacement sensor;
3) based on Gaussian regression, establishing a primary GPR error model which takes displacement data of three laser displacement sensors as input variables and takes errors of each position point in the directions of an X axis, a Y axis and a Z axis as dependent variables, and further solving error residual quantity;
adopting a Gaussian process regression method in machine learning, selecting a kernel function as a rational quadratic kernel function, defining the Gaussian process as the formula (2),
f(x)～GP(m(x),k(x,x′)) (2)
where m (x) is a mean function and k (x, x') is a covariance function;
for the multiple nonlinear regression problem, a model is set as formula (3),
y＝f(x)+ε (3)
in the formula: x is the input vector; y is the output value; ε -noise;
assuming that the noise satisfies a random distribution, ε -N (0, σ)n 2) Obtaining prior distribution of output value y as formula (4), observed value y*And a predicted value f*The joint prior distribution of (a), as in equation (5),
Figure FDA0003179201930000021
Figure FDA0003179201930000022
in the formula: k (X, X) -an nxn order symmetric positive definite covariance matrix, K (X, X)*) -test point x*Covariance matrix of order n X1, K (X) with input X*,x*) -test point x*(ii) its own covariance; i isn-an identity matrix of order n,. sigman-standard deviation of the random distribution of noise;
calculate the predicted value f*The posterior distribution of (2) is shown in the formula (6), and the predicted value f*The mean and variance of (A) are respectively expressed by the formula (7) and the formula (8),
Figure FDA0003179201930000023
Figure FDA0003179201930000024
Figure FDA0003179201930000025
in the formula:
Figure FDA0003179201930000026
-test point x*Corresponding predicted value f*The mean value of (a); cov (f)*)——f*The variance of (a);
based on the Gaussian Process Regression (GPR) method, data of three laser displacement sensors are used as input, error components of predicted point coordinates in the X direction, the Y direction and the Z direction are respectively used as output, and a GPR error model is generated;
selecting a rational quadratic kernel function as the kernel function of the Gaussian process regression, wherein the kernel function is in the form of formula (9),
Figure FDA0003179201930000027
4) removing a plurality of points with the spatial error larger than a critical value in the step 3), selecting a Matern2/5 kernel function to carry out secondary Gaussian process regression, establishing a secondary GPR position error calculation model of three laser displacement sensor data and position point coordinate errors, and further obtaining the coordinate error of each position point;
the quadratic GPR selects the Matern5/2 kernel function, which is shown in equation (10),
Figure FDA0003179201930000031
in the formula: ζ -smoothing coefficient; Gamma-Gamma function; h is a Bassel function, and when the smoothing coefficient is 0.5, the function is an exponential kernel; when the smooth coefficient is infinite, the coefficient is a Gaussian kernel;
5) calculation of the center of sphere coordinates of the non-contact R-test: preliminarily obtaining the spherical center coordinates of each position point under the workpiece coordinate system by performing coordinate calculation through the conversion matrix in the step 2), and compensating the preliminarily obtained spherical center coordinates through the spherical center coordinate errors of each position point obtained by calculating the Gaussian regression model twice in the step 3) and the step 4) to obtain the accurate spherical center coordinates of the non-contact R-test;
6) and (3) using the sphere center coordinates calculated in the step 5) in the calculation process of the machine tool key errors of the RTCP parameters and the geometric error thermal errors of the five-axis machine tool.
2. The non-contact R-test sphere center coordinate calibration method adopting the laser displacement sensor as claimed in claim 1, wherein: when the dots are discarded in the step 4, the dots with large spatial errors are removed, the original data are kept as much as possible, and the critical value is selected to be 0.7 μm.
CN202110841893.2A 2021-07-26 2021-07-26 Non-contact R-test sphere center coordinate calibration method adopting laser displacement sensor Active CN113532275B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110841893.2A CN113532275B (en) 2021-07-26 2021-07-26 Non-contact R-test sphere center coordinate calibration method adopting laser displacement sensor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110841893.2A CN113532275B (en) 2021-07-26 2021-07-26 Non-contact R-test sphere center coordinate calibration method adopting laser displacement sensor

Publications (2)

Publication Number Publication Date
CN113532275A CN113532275A (en) 2021-10-22
CN113532275B true CN113532275B (en) 2022-05-06

Family

ID=78120701

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110841893.2A Active CN113532275B (en) 2021-07-26 2021-07-26 Non-contact R-test sphere center coordinate calibration method adopting laser displacement sensor

Country Status (1)

Country Link
CN (1) CN113532275B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114252036B (en) * 2021-12-15 2022-10-25 成都飞机工业（集团）有限责任公司 Contact type R-Test instrument calibration method adopting ball head displacement sensor
CN114234877B (en) * 2022-02-28 2022-06-14 成都飞机工业（集团）有限责任公司 Displacement sensor vector calibration method for R-test instrument

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW201326742A (en) * 2011-12-29 2013-07-01 Prec Machinery Res Dev Ct Geometric error estimation method of five-axis tool machine
CN109032069A (en) * 2018-07-19 2018-12-18 西南交通大学 A kind of contactless R-test measuring instrument sphere centre coordinate calculation method using eddy current displacement sensor
CN109115095A (en) * 2018-08-03 2019-01-01 成都天佑创软科技有限公司 A kind of structure parameter optimizing method of contactless R-test measuring instrument
CN109357631A (en) * 2018-11-30 2019-02-19 厦门大学 A kind of measuring system center scaling method based on laser displacement sensor
KR20200037729A (en) * 2018-10-01 2020-04-09 가천대학교 산학협력단 Apparatus for Safety Analysis of Tunnel Concrete Structures Using 3D GPR and Method Using The Same
CN111854658A (en) * 2020-07-22 2020-10-30 四川大学 R-test precision ball head detection device and calibration method thereof
CN112013766A (en) * 2020-08-31 2020-12-01 华中科技大学 Non-contact R-test structural parameter redundancy-free calibration method
CN113031514A (en) * 2021-03-18 2021-06-25 西南交通大学 R-test calibration uncertainty evaluation method based on metrology

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW201326742A (en) * 2011-12-29 2013-07-01 Prec Machinery Res Dev Ct Geometric error estimation method of five-axis tool machine
CN109032069A (en) * 2018-07-19 2018-12-18 西南交通大学 A kind of contactless R-test measuring instrument sphere centre coordinate calculation method using eddy current displacement sensor
CN109115095A (en) * 2018-08-03 2019-01-01 成都天佑创软科技有限公司 A kind of structure parameter optimizing method of contactless R-test measuring instrument
KR20200037729A (en) * 2018-10-01 2020-04-09 가천대학교 산학협력단 Apparatus for Safety Analysis of Tunnel Concrete Structures Using 3D GPR and Method Using The Same
CN109357631A (en) * 2018-11-30 2019-02-19 厦门大学 A kind of measuring system center scaling method based on laser displacement sensor
CN111854658A (en) * 2020-07-22 2020-10-30 四川大学 R-test precision ball head detection device and calibration method thereof
CN112013766A (en) * 2020-08-31 2020-12-01 华中科技大学 Non-contact R-test structural parameter redundancy-free calibration method
CN113031514A (en) * 2021-03-18 2021-06-25 西南交通大学 R-test calibration uncertainty evaluation method based on metrology

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
一种非接触式R-test测量仪球心坐标计算方法;彭炳康等;《机械设计与制造》;20210331(第3期);第165-171页 *

Also Published As

Publication number Publication date
CN113532275A (en) 2021-10-22

Similar Documents

Publication Publication Date Title
CN113532275B (en) Non-contact R-test sphere center coordinate calibration method adopting laser displacement sensor
Weckenmann et al. Functionality-oriented evaluation and sampling strategy in coordinate metrology
CN109032069B (en) Non-contact R-test measuring instrument sphere center coordinate calculation method adopting eddy current displacement sensor
Cappa et al. A sub-nanometre spindle error motion separation technique
CN110245395B (en) Method, system and medium for monitoring cylindrical shape error with spatial correlation
Chen et al. A novel multi-probe method for separating spindle radial error from artifact roundness error
Moroni et al. Optimal inspection strategy planning for geometric tolerance verification
Ren et al. A task specific uncertainty analysis method for least-squares-based form characterization of ultra-precision freeform surfaces
Liu et al. A coaxiality measurement model based on multi-systematic errors separation for turbine shaft
Bourdet et al. Effects of data point distribution and mathematical model on finding the best-fit sphere to data
CN109614758B (en) Method for monitoring circular shape error with spatial correlation
Stępień Testing the accuracy of surface roughness measurements carried out with a portable profilometer
CN111797482A (en) Sensitive error weight calculation method for scanning measurement of cartesian coordinate system profile
Yu et al. An approach for machining distortion measurements and evaluation of thin-walled blades with small datum
CN113570147B (en) Method and equipment for rapidly predicting machining errors of thin-wall parts in multiple modes
CN112179550B (en) Optimized arrangement method of four-fulcrum piezoelectric dynamometer
CN108375337B (en) Robot and method and device for measuring relative pose of process equipment of robot
CN110021027B (en) Edge cutting point calculation method based on binocular vision
Chen et al. Criteria of determining the P/T upper limits of GR&R in MSA
Poniatowska Optimizing sampling parameters of cmm data acquisition for machining error correction of freeform surfaces
Zhang et al. Spindle radial accuracy evaluation and fault diagnosis based on a normal distribution
Abiline et al. On the uncertainty of measurements by measuring the form of a surface
Ninggar et al. Sample Size Analysis for the Measurement of Straightness
Tian et al. Notice of Retraction: Comparing evaluated precision of straightness error among two spot, least square method and minimum envelope zone method
Fischer et al. Tooth flank approximation with root point iteration–potentials and limits in gear metrology

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant