CN105043259A - Numerical control machine tool rotating shaft error detection method based on binocular vision - Google Patents
Numerical control machine tool rotating shaft error detection method based on binocular vision Download PDFInfo
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Abstract
The invention discloses a numerical control machine tool rotating shaft error detection method based on binocular vision, belongs to the machine tool precision detection technology field and relates to a double rotating shaft geometry error detection and identification method of a five-axes numerical control machine tool. The method adopts a high-resolution binocular vision system. Position information of a marking point which is pasted on a machine tool rotation table surface is collected. Through camera calibration, image segmentation, marking point extraction and a machine tool rotating shaft error identification model, detection acquisition of a two-position error and a two-angle error of a machine tool rotating shaft is realized. Rapid measurement of a geometrical parameter is completed. A circular marking point is used. An image processing program is simple, feature extraction precision is high, robustness is good and measurement is rapid and convenient. Simultaneously, a problem that numerical control machine tool rotating shaft installation error detection and identification are difficult is solved and a new direction is provided for a machine tool error detection and identification technology.
Description
Technical Field
The invention belongs to the technical field of machine tool precision detection, and relates to a double-rotating-shaft geometric error detection and identification method of a five-shaft numerical control machine tool.
Background
In the fields of aviation, aerospace, national defense industry and the like, the requirements on efficient and high-precision manufacturing are higher and higher. Particularly, aiming at parts such as an engine impeller with a complex structure, die manufacturing and the like, the five-axis numerical control machine can realize flexible control of position and direction, and is a technology widely applied at present. However, compared with a three-axis numerical control machine tool, the five-axis numerical control machine tool has errors of three linear axes, and also increases errors of two rotation axes, which causes an increase of machine tool error terms, and is unavoidable. The rotating shaft is an important component of a five-axis numerical control machine tool, and is a main source of quasi-static errors and dynamic errors of the machine tool due to the lack of a precision calibration and error compensation method. Therefore, the error of the rotating shaft is regularly checked and calibrated, the precision of the machine tool can be maintained, and meanwhile, the foundation is laid for precision manufacturing.
At present, the technology of numerical control machine error detection mainly includes: a real object reference measurement method, a laser interferometer, a cue instrument, a laser tracker and the like. The patent number CN102476323A 'novel numerical control machine error detector' invented by Dalian Chunda technology Limited company Donghai invents an error detector based on circular track, and evaluates the machine tool performance by analyzing the error of circular track interpolation. However, this method is inconvenient to recognize errors, resulting in difficulty in error compensation. The invention discloses a machine tool error real-time detection technology based on a laser tracker, which is invented by a patent number CN103143984A dynamic machine tool error compensation method based on the laser tracker of Chongqing university Toonaibao and the like, and the method is simple and convenient, but has higher cost. The laser interferometer has high measurement precision, but the operation is more complicated. In summary, the current method is mostly used for error detection of linear axes, and is high in cost, and is not suitable for detection and identification of errors of rotating axes. Therefore, it is necessary to develop a convenient, fast and low-cost rotation axis error detection and identification technique.
Disclosure of Invention
The invention provides a method for measuring the installation error of a rotating shaft of a machine tool based on binocular vision, which aims to solve the technical problem of overcoming the prior art. And attaching a plurality of groups of reflective coding mark points on the surface of the rotating shaft to be detected, controlling the rotating shaft of the machine tool to rotate at a fixed angle by the numerical control system, collecting the reflective coding mark points at each angle by utilizing binocular vision, and obtaining three-dimensional position information of the coding mark points. Based on the position information of the mark points, fitting a space circle by adopting a least square method, obtaining the position coordinates of the space circle, and comparing the position coordinates with the central position of an ideal axis to obtain the central linear error of the rotating axis to be measured; meanwhile, a space plane is fitted by using the positions of the mark points, and the normal vector of the plane is compared with the ideal rotating shaft axis vector, so that the angle error of the installation of the rotating shaft can be measured. The method adopts the circular mark points, so that the image processing procedure is simple, the feature extraction precision is high, the robustness is good, and the measurement is rapid and convenient. Meanwhile, the method solves the problem that the mounting error of the rotating shaft of the numerical control machine tool is difficult to detect and identify, and provides a new direction for the machine tool error detection and identification technology.
The technical scheme adopted by the invention is a method for detecting errors of a rotating shaft of a numerical control machine tool based on binocular vision, which is characterized in that a high-resolution binocular vision system is adopted, the position information of a circular ring coding mark point attached to the surface of a rotary table of the machine tool is collected, and the vision system collects the position information once every time the rotary table rotates a certain angle until the rotary table rotates a circle. Finally, the detection and acquisition of two position errors and two angle errors of the machine tool rotating shaft are realized through camera calibration, image segmentation, mark point extraction and a machine tool rotating shaft error identification model, so that 4 installation errors of the machine tool rotating shaft are obtained, and the rapid measurement of geometric parameters is completed; the detection method comprises the following specific steps:
(1) calibration of a camera
The invention adopts a binocular vision calibration method based on a high-precision checkerboard target, which is proposed by Zhangyingyou et al;
firstly, determining internal and external parameters of two cameras by using a Zhang calibration method, then performing three-dimensional reconstruction on a checkerboard target corner point, establishing a function f (x) according to the deviation of a corner point reconstructed coordinate and an actual coordinate, and performing integral optimization on the internal and external parameters; as follows:
f(x)＝(xp-xi)2+(yp-yi)2+(zp-zi)2(1)
wherein: x is the number ofp，yp，zpIs the actual coordinate of each corner point, and xi，yi，ziFor reconstructing the coordinates of each corner point, an objective function f (x) is established as follows:
wherein,optimizing the objective function F (x) by using an LM (least squares) method for the sum of squares of all point deviation functions to obtain a global optimal solution of internal and external parameters;
(2) image feature segmentation
Firstly, carrying out noise reduction and filtering processing on an image, and then, preliminarily separating all target characteristics from a background by using a gray-scale valve value method, wherein the gray-scale valve value method has a corresponding formula:
wherein, G (x, y) is the gray value corresponding to the pixel point of the image (x, y), T represents the selected gray threshold value, G1、G2A background set and a feature label set are used; then, carrying out connected region marking on the feature mark set, and removing the image by using the region area as a threshold valueThe corresponding formula is as follows:
n is n connected regions, gi(x, y) is the area of the ith connected region, and S is a connected region area threshold value; if the area of the connected region is smaller than S, setting the connected region as a background;
(3) extraction of feature labels
1) And (3) extracting the center of the coding point:
firstly, 8 connected regions are adopted to mark connected regions in the image, and then curvature constraint is utilized to remove the non-interesting connected regions with larger curvature and smaller curvature, and the corresponding formula is as follows:
where i 1,2.. n is n connected regions, gt (i) is the eccentricity of the i-th connected region, e1,e2For the eccentricity threshold, l (i) ═ 0 indicates that the ith connected component is set as the background; thus, an accurate coding mark point image can be obtained; then, obtaining the center coordinates of the coding mark points by using a centroid algorithm;
2) and (3) identifying the coding points:
the invention adopts a ring coding mark point, the center of the ring coding is a ring mark point 1, and the periphery of the mark point is a segment ring area concentric with the mark point, which is used for representing the identity information of the ring coding and is called a coding band 2; the circular ring is divided into 15 parts according to the angle on average, each part is 24 degrees, and the circular ring is equivalent to a binary bit; each digit takes the front scene as white, the back scene as black, and the corresponding binary code is '1' or '0'; starting from the center of the mark point, scanning solid and hollow code bands in a certain direction, wherein white represents solid, black represents hollow, the solid code band is scanned and marked as 1, and the hollow code band is scanned and marked as 0; if the code band is not scanned, rescanning from the center; after scanning for one week, the code value sequence of the whole coding point is completely read out to form a binary sequence, and each binary sequence corresponds to a decimal integer, so that the identity information of each coding point is obtained;
after decoding, storing the pixel coordinates of the same coding point of each angle under a file according to the identity information of different coding marking points, and sequentially obtaining the pixel coordinates of left and right images of even marking points; then, reconstructing the three-dimensional coordinates of each mark point by using the internal and external parameters of the camera obtained by the Zhang calibration method;
(4) machine tool rotation axis error identification
The error of the rotating shaft of the numerical control machine tool mainly comprises two error sources, namely a connection error and a volume error; the former is independent of the machine tool command position, usually caused by the installation deviation of a rotating shaft, and the latter is related to the machine tool command position and is influenced by the machining precision of machine tool parts; aiming at the connection error of a machine tool rotating shaft, the invention provides a machine tool rotating shaft error detection and identification method based on binocular vision; the joint error has 4 items including 2 linear position errors and 2 angle errors;
according to the obtained three-dimensional coordinates of the coding points under different angles in the visual coordinate system, fitting a plane by using a least square method, and establishing a plane equation:
Ax+By+Cz+D＝0(6)
wherein A, B, C, D is a plane equation coefficient; after simplification, the following can be obtained:
to achieve the planar fit, an objective function f (x) is established:
wherein, (xi,yi,zi) (i ═ 1,2,3.. n) is three-dimensional coordinates of the n encoding mark points in a visual coordinate system; from this a fitted plane can be obtained, and the normal vector to this plane. Comparing the normal vector of the fitting plane with the normal vector of the ideal plane, and solving to obtain 2 angular errors of the connection error of the rotating shaft;
for identifying the linear position error of the rotary shaft connection error, every two points are connected into a straight line L according to the position relation of the coding points1(ii) a When the rotating shaft rotates according to a certain angle, the straight line rotates along with the rotating shaft to form a straight line L2And a straight line L1And a straight line L2Intersect at a point P1(ii) a Sequentially, the rotating shaft rotates for a circle to form n straight lines, and every two straight lines intersect at a point PiN/2, taking an average value P of the coordinates of the points, and regarding the P as the center of an actual circle; the linear position error of the connecting error of the rotating shaft of the machine tool can be obtained by comparing the coordinates of the actual circle center and the ideal circle center:
er(x)＝P(x)-Pideal(x)(9)
er(y)＝P(y)-Pideal(y)(10)
wherein, er (x), er (y) are errors of linear positions of the rotating shaft in the X, Y direction, P (x), P (y) are X, Y coordinates of the actual center of the rotating shaft, P (x)ideal(x),Pideal(y) X, Y coordinates of the center of the ideal axis of rotation;
the method has the advantages that the method utilizes the reflective coding mark points to realize the detection and identification of the 4 connection errors of the rotating shaft of the numerical control machine tool, and has the advantages of convenience, rapidness, good robustness, strong noise resistance, no need of laser collimation and the matching of other shafts, and the like. The method effectively improves the efficiency of error detection of the machine tool rotating shaft, avoids a complex measuring process and a complex identification model, and provides a quick and convenient method for error detection of the numerical control machine; and meanwhile, the basis and the direction are provided for other error detection of the machine tool.
Drawings
Fig. 1 is a model diagram of a machine tool error detection device. Wherein, the device comprises a left camera 1, a right camera 2, a reflective coding mark point 3, a machine tool rotating platform 4 and a numerical control machine 5.
Fig. 2 is a circular ring coding mark dot diagram. Wherein, 1-round mark point and 2-coding band.
FIG. 3 is a schematic diagram illustrating the error recognition of the rotational angle axis of the machine tool. 1-ideal turntable plane, 2-actual turntable plane, 3-actual turntable normal vector and 4-coding mark point,1machine tool rotation axis and spindleThe angular error of the virtual turntable normal vector 3,2-angular error of the machine rotation axis with respect to the Z axis.
FIG. 4 is a schematic diagram of machine tool spindle position error identification. 1-ideal turntable, 2-actual turntable, 3-actual turntable center, 4-coding mark point, 5-ideal turntable center,1-linear position error of the machine tool rotation axis with respect to the X axis,2-linear position error of machine tool rotation axis and Y axis.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings. FIG. 1 is a model diagram of a machine tool error detection device based on binocular vision. The method collects the coordinate information of the coding mark points on the surface of the tested turntable through the left camera 1 and the right camera 2, and identifies the link error of the rotating shaft through processing and solution.
Firstly, a measuring device is installed, a left camera 1 and a right camera 2 are installed above a rotating shaft, the left high-speed camera 1 and the right high-speed camera 2 are fixed, the positions are adjusted to enable a measuring view field to be in a public view field of the left high-speed camera 1 and the right high-speed camera 2, and the brightness of a light source is adjusted to improve the brightness of a measuring space; then, pasting the reflective mark points 3 on the surface of the rotary table 4 at will, and controlling the machine tool to rotate according to a certain angle, wherein the left camera 1 and the right camera 2 shoot once every time the machine tool rotates once until the rotary table 4 rotates for a circle; and finally, the image workstation performs the work of binocular camera calibration, image segmentation, feature extraction, error identification and the like.
The invention adopts two high-resolution cameras 1 and 2 to shoot the motion condition of an object, the models of the two cameras are VA-29M cameras, the resolution is as follows: 6576 × 4384, target surface size: 35mm, frame frequency: 5 fps. The lens model is a Canon EF 24-70 mmf/2.8LIIUSM zoom lens, the parameters are as follows, and the focal length of the lens is as follows: 24-70, maximum aperture: f2.8, lens weight: 805g, lens size: 88.5mm by 113 mm. The shooting conditions were as follows: the picture pixel is 6576 × 4384, the focal length of the lens is 50mm, the object distance is 460mm, and the field of view is about 200mm × 200 mm.
(1) Performing calibration of high speed cameras
The invention adopts a camera calibration method based on a two-dimensional plane checkerboard target, which is proposed by Zhangyingyou et al, as a basis, calibration is carried out to obtain the internal parameters K, the external parameters [ RT ] and the distortion coefficient of two high-speed cameras, and then a Levenberg-Marquardt (LM) method is applied to optimize a formula (2), so as to obtain the global optimal solution of the internal and external parameters of each camera of a binocular vision system, wherein the calibration result is shown in a table 1:
TABLE 1 calibration results
(2) Image feature segmentation
And (3) preprocessing the acquired image by utilizing a gray level threshold value method, and preliminarily separating the coding mark points from the background through a gray level threshold value according to a formula (3). And then, carrying out connected region marking on the coding mark set, removing the non-interesting connected regions in the image by using the region area as a threshold value, and finally realizing image segmentation.
(3) Extraction of feature labels
And marking the connected regions in the image by using 8 connected regions, and then removing the non-interesting connected regions with larger curvature and smaller curvature by using curvature constraint to obtain a clear image. Meanwhile, a centroid algorithm is utilized to obtain the center coordinates of the coding mark points; fig. 2 is a circular encoding mark dot diagram showing that the circular mark dots 1 are white, and the surrounding annular area shows that the identification information of the encoding mark dots is the encoding zone 2. And starting from the center of the circle mark point, scanning the solid and hollow coding bands in a clockwise direction. Wherein, white represents solid, black represents hollow, and scanning the solid code band is marked as 1, and the hollow code band is marked as 0. After one scan cycle, the code value sequence of the entire code point is read out to form a binary sequence 001100111011111 and converted into a decimal integer 6607, thereby obtaining the identity information of each code point. In addition, through binocular reconstruction, three-dimensional coordinates of the encoded marker points can be obtained.
(4) Machine tool rotation axis error identification
In the invention, the machine tool is controlled to rotate once every 5 degrees, the machine tool rotates 360 degrees in total, left and right images are collected once at each angle, and the three-dimensional coordinates of the coding points are reconstructed. In order to realize the detection and identification of the connection error of the rotating shaft of the machine tool, plane fitting and circle center calculation are respectively carried out. First, as shown in fig. 3, according to the obtained three-dimensional coordinates of the encoding point 4 in the visual coordinate system under different angles, the marking point plane 2 is fitted by the least square method using the formulas (6), (7) and (8), and the normal vector 3 of the plane is obtained through calculation. Comparing the normal vector 3 of the fitting plane with the normal vector of the ideal plane, taking the Z-axis vector as the normal vector of the ideal plane, and solving the angle error between the machine tool rotating shaft and the normal vector 3 of the actual rotary table1Angular error of machine tool rotation axis and Z axis2Two angular errors.
In order to identify the linear position error of the rotating shaft connection error, 2 coding points are selected according to the position relation of the coding points to form 1 straight line which is marked as an initial straight line L1. The machine tool rotates once every 5 degrees, a new straight line can be formed when the machine tool does not rotate once, the machine tool rotates for a circle in sequence, each initial straight line can form 71 new straight lines, the intersection point of the straight lines is regarded as a circle center O, as shown in figure 4, and finally, an average value is obtained through three groups of experiments to determine an accurate circle center. Comparing the coordinates of the actual circle center 3 and the ideal circle center 5 by using the formulas (9) and (10), and finally obtaining the linear position error of the machine tool rotating shaft and the X axis1Linear position error of machine tool rotation axis and Y axis2。
The invention utilizes binocular vision to detect the coded mark point information and realizes the error detection and identification of the machine tool rotating shaft by establishing a simpler error identification model. The method has the advantages of convenience, rapidness, good robustness, strong noise resistance, no need of laser collimation and other shaft matching, and the like, effectively improves the efficiency of error detection of the machine tool rotating shaft, and provides a foundation and a direction for other error detection of the machine tool.
Claims (1)
1. The invention relates to a method for detecting errors of a rotating shaft of a numerical control machine tool based on binocular vision, which is characterized in that a high-resolution binocular vision system is adopted to collect position information of a circular ring coding mark point attached to the surface of a rotary table of the machine tool, and the vision system collects the position information once every time the rotary table rotates a certain angle until the rotary table rotates a circle. Finally, the detection and acquisition of two position errors and two angle errors of the machine tool rotating shaft are realized through camera calibration, image segmentation, mark point extraction and a machine tool rotating shaft error identification model, and the rapid measurement of geometric parameters is completed; the detection method comprises the following specific steps:
(1) calibration of a camera
The invention adopts a binocular vision calibration method based on a high-precision checkerboard target, which is proposed by Zhangyingyou et al;
firstly, determining internal and external parameters of two cameras by using a Zhang calibration method, then performing three-dimensional reconstruction on a checkerboard target corner point, establishing a function f (x) according to the deviation of a corner point reconstructed coordinate and an actual coordinate, and performing integral optimization on the internal and external parameters; as follows:
f(x)＝(xp-xi)2+(yp-yi)2+(zp-zi)2(1)
wherein: x is the number ofp，yp，zpIs the actual coordinate of each corner point, and xi，yi，ziFor reconstructing the coordinates of each corner point, an objective function f (x) can be established as follows:
wherein,optimizing the objective function F (x) by using an LM (least squares) method for the sum of squares of all point deviation functions to obtain a global optimal solution of internal and external parameters;
(2) image feature segmentation
Firstly, carrying out noise reduction and filtering processing on an image, and preliminarily separating all target characteristics from a background by using a gray-scale valve value method, wherein the gray-scale valve value method has a corresponding formula:
wherein, G (x, y) is the gray value corresponding to the pixel point of the image (x, y), T represents the selected gray threshold value, G1、G2A background set and a feature label set are used; performing connected region marking on the feature marker set, and removing uninteresting connected regions in the image by using the region area as a threshold valueThe domain, the corresponding formula is as follows:
n is n connected regions, gi(x, y) is the area of the i-th connected region, SIs a threshold value of the area of the connected region; if the area of the connected region is smaller than S, setting the connected region as a background;
(3) extraction of feature labels
1) And (3) extracting the center of the coding point:
firstly, 8 connected regions are adopted to mark connected regions in the image, and then curvature constraint is utilized to remove the non-interesting connected regions with larger curvature and smaller curvature, and the corresponding formula is as follows:
where i 1,2.. n is n connected regions, gt (i) is the eccentricity of the i-th connected region, e1,e2For the eccentricity threshold, l (i) ═ 0 indicates that the ith connected component is set as the background; thus, an accurate coding mark point image is obtained; obtaining the center coordinates of the coding mark points by using a centroid algorithm;
2) and (3) identifying the coding points:
the invention adopts a ring coding mark point, the center of the ring coding is a ring mark point (1), and the periphery of the mark point is a segment ring area concentric with the mark point and used for representing the identity information of the ring coding, which is called a coding band (2); the circular ring is divided into 15 parts according to the angle on average, each part is 24 degrees, and the circular ring is equivalent to a binary bit; each digit takes the front scene as white, the back scene as black, and the corresponding binary code is '1' or '0'; starting from the center of the mark point, scanning the solid code band and the hollow code band according to a certain direction, recording the solid code band as 1 and the hollow code band as 0, and if the code band is not scanned, starting to scan again from the center; after scanning for one week, the code value sequence of the whole coding point is completely read out to form a binary sequence, and each binary sequence corresponds to a decimal integer, so that the identity information of each coding point is obtained;
after decoding, storing the pixel coordinates of the same coding point of each angle under a file according to the identity information of different coding marking points, and sequentially obtaining the pixel coordinates of left and right images of even marking points; then, reconstructing the three-dimensional coordinates of each mark point by using the internal and external parameters of the camera obtained by the Zhang calibration method;
(4) machine tool rotation axis error identification
The method carries out detection and identification aiming at the connection error of the machine tool rotating shaft, and comprises 2 linear position errors and 2 angle errors;
according to the obtained three-dimensional coordinates of the coding points under different angles in the visual coordinate system, fitting a plane by using a least square method, and establishing a plane equation:
Ax+By+Cz+D＝0(6)
wherein A, B, C, D is a plane equation coefficient; after simplification, the following can be obtained:
to achieve the planar fit, an objective function f (x) is established:
wherein, (xi,yi,zi) (i ═ 1,2,3.. n) is three-dimensional coordinates of the n encoding mark points in a visual coordinate system; from this a fitted plane can be obtained, and the normal vector to this plane. Comparing the normal vector of the fitting plane with the normal vector of the ideal plane, and solving to obtain 2 angular errors of the connection error of the rotating shaft;
for identifying the linear position error of the rotary shaft connection error, every two points are connected into a straight line L according to the position relation of the coding points1(ii) a When the rotating shaft rotates according to a certain angle, the straight line rotates along with the rotating shaft to form a straight line L2And a straight line L1And a straight line L2Intersect at a point P1(ii) a Sequentially, the rotating shaft rotates for a circle to form n straight lines, and every two straight lines intersect at a point PiI is 1,2,3 … n/2, taking the average value P of the coordinates of the points, and regarding the P as the center of an actual circle; the linear position error of the connecting error of the rotating shaft of the machine tool can be obtained by comparing the coordinates of the actual circle center and the ideal circle center:
er(x)＝P(x)-Pideal(x)(9)
er(y)＝P(y)-Pideal(y)(10)
wherein, er (x), er (y) are errors of linear positions of the rotating shaft in the X, Y direction, P (x), P (y) are X, Y coordinates of the actual center of the rotating shaft, P (x)ideal(x),PidealAnd (y) is X, Y coordinates of the center of the ideal rotation axis.
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| CN111649671A (en) * | 2020-06-11 | 2020-09-11 | 中国航空工业集团公司北京航空精密机械研究所 | Multi-axis vision measurement system and calibration method for rotation axis position of pitching table |
| CN112611318A (en) * | 2020-12-03 | 2021-04-06 | 深圳数马电子技术有限公司 | Method and device for measuring motion axis error |
| CN112991464A (en) * | 2021-03-19 | 2021-06-18 | 山东大学 | Point cloud error compensation method and system based on three-dimensional reconstruction of stereoscopic vision |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5768759A (en) * | 1996-11-19 | 1998-06-23 | Zevatech, Inc. | Method and apparatus for reflective in-flight component registration |
| CN103323229A (en) * | 2013-07-08 | 2013-09-25 | 重庆工业职业技术学院 | Rotation axis error detection method of five-axis numerical control machine tool based on machine vision |
| CN103878641A (en) * | 2014-03-14 | 2014-06-25 | 浙江大学 | Rotating shaft geometric error identification method commonly used for five-axis numerical control machine tool |
| CN103913131A (en) * | 2014-04-14 | 2014-07-09 | 大连理工大学 | Free curve method vector measurement method based on binocular vision |
| CN104308657A (en) * | 2014-10-14 | 2015-01-28 | 浙江大学 | Machine tool rotating shaft geometry error six-circle identifying method based on measuring of ball bar instrument |
-
2015
- 2015-08-25 CN CN201510527601.2A patent/CN105043259B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5768759A (en) * | 1996-11-19 | 1998-06-23 | Zevatech, Inc. | Method and apparatus for reflective in-flight component registration |
| CN103323229A (en) * | 2013-07-08 | 2013-09-25 | 重庆工业职业技术学院 | Rotation axis error detection method of five-axis numerical control machine tool based on machine vision |
| CN103878641A (en) * | 2014-03-14 | 2014-06-25 | 浙江大学 | Rotating shaft geometric error identification method commonly used for five-axis numerical control machine tool |
| CN103913131A (en) * | 2014-04-14 | 2014-07-09 | 大连理工大学 | Free curve method vector measurement method based on binocular vision |
| CN104308657A (en) * | 2014-10-14 | 2015-01-28 | 浙江大学 | Machine tool rotating shaft geometry error six-circle identifying method based on measuring of ball bar instrument |
Non-Patent Citations (1)
| Title |
|---|
| 刘巍等: "基于双目视觉的转动惯量测量方法", 《基于双目视觉的转动惯量测量方法》 * |
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