CN105241389A - Machine visual sense based detection system for blunt round radius of cutting edge of milling cutter - Google Patents
Machine visual sense based detection system for blunt round radius of cutting edge of milling cutter Download PDFInfo
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Abstract
The invention discloses a machine visual sense based detection system for the blunt round radius of a cutting edge of a milling cutter. The system comprises an image acquisition module, an image processing module, a system calibration system and an image measuring module. An image of a calibration plate and an image of a cutting edge of a milling cutter are photographed by a CCD industrial camera and a lens, and an acquired analog signal is converted into a digital signal, which is then saved in a computer. The image processing module processes the images stored in the computer, and extracts the contour of the rim of the cutting edge of a milling cutter. The system calibration system calibrates the image of a to-be-detected cutting edge of a milling cutter. The image measuring module establishes an ROI region, searches for the ROI region, determines the position of the rim through bilinear interpolation or a bilinear interpolation fixed point calculating method, and finally enables edge points to form a circle through fitting by means of a mean value method or an intermediate value method. The radius of the fitted circle is the blunt round radius of the milling cutter. The advantages of the system are that the processing speed is fast and the processing efficiency is high, the system is not influenced by human factors and surrounding environments, the obtained blunt round radius of the cutting edge of the milling cutter is high in precision, etc.
Description
Technical field
The invention belongs to image acquisition and processing technology, particularly relate to a kind of milling cutter cutting edge blunt round radius detection system based on machine vision.
Background technology
Can be there is the microdefects such as burr, little spring sword, saw kerf in ordinary rigid alloy slotting cutter, these microdefects can accelerate tool wear, have a strong impact on cutting ability and the serviceable life of cutter after fine grinding.The defect on cutting edge can be eliminated by milling cutter cutting edge passivation, increase cutting edge blunt round radius and improve tool surface smooth finish, thus make in cut the life-span of cutter, the stability of cutting and workpiece machined surface quality etc. have prolongation in various degree and raising, wherein the prolongation of cutter life is the most obvious.Milling cutter cutting edge passivation profile has become key issue urgently to be resolved hurrily in cut to the affecting laws of cutter cutting ability.After milling cutter cutting edge passivation, its blunt round radius reaches micron order, and this proposes requirements at the higher level to the precision of detection system.Traditional cutting edge roundness blunt round radius measurement means precision is low, efficiency is poor, environmental factor and human factor impact larger etc., can not meet the requirement of milling cutter cutting edge high-acruracy survey.
Summary of the invention
The technical problem to be solved in the present invention: a kind of milling cutter cutting edge blunt round radius detection system based on machine vision is provided, to solve, prior art is low on cutting edge roundness blunt round radius measurement means precision, efficiency is poor, environmental factor and human factor impact larger etc., the technical matterss such as milling cutter cutting edge high-acruracy survey requirement can not be met.
Technical solution of the present invention:
Based on a milling cutter cutting edge blunt round radius detection system for machine vision, it comprises:
Image capture module, by CCD industrial camera and lens shooting scaling board and milling cutter cutting edge image, is converted to digital signal by image pick-up card by the simulating signal collected and is deposited into computing machine;
Image processing module, processes the image stored in computing machine, extracts milling cutter cutting edge edge contour;
System calibrating module, first demarcates scaling board image by binaryzation, obtains the corresponding relation between the actual value of scaling board distance of center circle and pixel value, obtain the calibration coefficient K1 of scaling board, then corrected perspective distortion, finally demarcate tested milling cutter cutting edge image;
Image measurement module, set up a ROI region, searched in ROI region by mode from inside to outside or from outside to inside, the fixed-point calculation of bilinear interpolation or bilinear interpolation is used to determine marginal position, finally use mean value method or intermediate value method that marginal point is fitted to circle, fitting circle radius value is milling cutter blunt round radius.
Described image processing module comprises
Image enhaucament submodule, adopts histogram equalization and histogram specification to strengthen image;
Image filtering submodule, adopts medium filtering and low-pass filtering filtering image noise;
Iamge Segmentation submodule, adopting many threshold segmentation method and adaptive threshold split plot design segmentation image, is target area and background area two large regions by whole Iamge Segmentation;
Edge contour extracts submodule, uses Canny edge detection operator to extract milling cutter edge contour.
The algorithm that adaptive threshold split plot design adopts is maximum variance between clusters and entropy principle method.
Beneficial effect of the present invention:
The mode that image acquisition of the present invention adopts the substep of milling cutter cutting edge image and scaling board image to gather, ensure that milling cutter cutting edge image is identical with the enlargement ratio of scaling board image, meet the requirement of later stage system calibrating requirement, the image collected is passed through image enhancement processing, make up because of camera lens parameter and the undesirable problem of external light influence hypograph target and background visual effect, inevitable various undesired signal in gatherer process is eliminated by image filtering, finally extract clearly milling cutter edge contour, accurately to measure milling cutter cutting edge blunt round radius, eventually pass through system calibrating and parameter measurement obtains milling cutter cutting edge blunt round radius, the present invention processes picture automatically due to employing system, various compensation is carried out to picture, therefore there is the fast efficiency advantages of higher of processing speed, and not by impact that is artificial and surrounding environment, the milling cutter cutting edge blunt round radius obtained is made to have precision advantages of higher, the invention solves prior art low to cutting edge roundness blunt round radius measurement means precision, efficiency is poor, environmental factor and human factor impact larger etc., the technical matterss such as milling cutter cutting edge high-acruracy survey requirement can not be met.
Accompanying drawing explanation
Fig. 1 is present system structured flowchart;
Fig. 2 is image processing module structured flowchart of the present invention.
Embodiment
Based on a milling cutter cutting edge blunt round radius detection system for machine vision, it comprises:
Image capture module, by CCD industrial camera and lens shooting scaling board and milling cutter cutting edge image, is converted to digital signal by image pick-up card by the simulating signal collected and is deposited into computing machine; Image capture module is prerequisite and the basis of whole system, gathers the substep collection mainly comprising milling cutter cutting edge image and scaling board image, identical with the enlargement ratio of scaling board image to guarantee milling cutter cutting edge image, meets system calibrating requirement.
First under the back lighting of employing annular light source, CCD industrial camera is called and camera lens carries out shooting scaling board and milling cutter cutting edge image by the image acquisition function IMAQGrabAcquireVI in LabVIEW software vision and motion module IMAQVison module, then by image pick-up card, the simulating signal collected being converted to digital signal is input in calculator memory, is stored in hard disc of computer by image by Image Saving function IMAQWriteFile2VI.
Image processing module, processes the image stored in computing machine, extracts milling cutter cutting edge edge contour;
Described image processing module comprises
Image enhaucament submodule, adopts histogram equalization and histogram specification to strengthen image; Because of under camera lens parameter and external light influence in image acquisition, image object and background visual effect unsatisfactory, use algorithm for image enhancement improve image visual effect, the present invention mainly adopts histogram equalization and histogram specification to strengthen image; Histogram equalization function IMAQEqualizeVI is for realizing the equalization distribution of whole image intensity value.Histogram specification refers to and utilizes change image histogram function IMAQBCGlookupVI directly to reach change image histogram by changing these three parameters of brightness of image, contrast and gamma value, the span of its three values is respectively 0 to 255,0 to 60,0.1 to 10, and default value is respectively 128,45,1.
Image filtering submodule, adopts medium filtering and low-pass filtering filtering image noise; Inevitably can be subject to the interference of various noise in image acquisition simultaneously, partial noise signal can be there is in the image gathered, affect the extraction of image outline, therefore the present invention uses Image filter arithmetic filtering image noise, and the present embodiment adopts medium filtering and low-pass filtering filtering image noise.Medium filtering function IMAQNthorderVI is used for the medium filtering of spatial domain, and its Filtering Template size is variable, and default value is 3 × 3; Low-pass filter function IMAQLowpassVI is used for low-pass filtering.
Iamge Segmentation submodule, adopting many threshold segmentation method and adaptive threshold split plot design segmentation image, is target area and background area two large regions by whole Iamge Segmentation; Image improves picture quality after Image semantic classification, but each grey scale pixel value in image is continuous gradation, and system accurately cannot determine milling cutter cutting edge contour and scaling board profile.Therefore, according to the thought of " binaryzation ", use threshold segmentation method by image intensity value binaryzation, thus be target (milling cutter cutting edge) and background two large regions by whole Iamge Segmentation.Many threshold segmentation method and adaptive threshold split plot design segmentation image will be adopted in system.Wherein adaptive threshold split plot design uses specific algorithm to calculate the threshold values adapted with image according to the intensity profile of image, thus segmentation image, its algorithm mainly adopts maximum variance between clusters and entropy principle method etc.Many threshold segmentation function IMAQMultiThresholdVI is used for many threshold segmentation, and it uses one-dimension array to arrange threshold segmentation section, uses three the numerical value input controls comprised in race to represent minimum threshold values T1, the highest threshold values T2 and alternative threshold values P respectively.Adaptive threshold segmentation function IMAQAutoBThreshold2VI is used for adaptive threshold segmentation, and its method has entropy principle method, maximum variance between clusters etc.
Edge contour extracts submodule, uses Canny edge detection operator to extract milling cutter edge contour.After Iamge Segmentation image object and background segment obvious, Canny edge detection operator is finally used to extract milling cutter edge contour, accurately to measure milling cutter cutting edge blunt round radius, Canny edge indicator function IMAQCannyEdgeDectionVI is used for Canny rim detection.
Because image processing algorithm various in above-mentioned steps has two or more, therefore native system will use double condition structure and shift register in While circulation, make this mould can select arbitrarily corresponding algorithm soon in operational process, the result of various algorithm is analyzed.
System calibrating module, first demarcates scaling board image by binaryzation, obtains the corresponding relation between the actual value of scaling board distance of center circle and pixel value, obtain the calibration coefficient K1 of scaling board, then corrected perspective distortion, finally demarcate tested milling cutter cutting edge image;
Milling cutter cutting edge blunt round radius reaches micron order, and accuracy requirement is very high.Measure for realizing micron order blunt round radius, scaling board selects center of circle array calibrating plate, and its physical dimension is 35mm × 35mm, and round dot number is 50 × 50, and circular diameter is 0.25mm, and distance of center circle is 0.5mm, and manufacturing accuracy is 1 μm.
System calibrating principle is: the distance of center circle physical length of center of circle array calibrating plate is that M(is in units of millimeter mm), the distance of center circle Pixel Dimensions in the image of collected by camera is used to be that N(is in units of number of pixels), then the ratio of physical size M and Pixel Dimensions N is exactly the calibration coefficient of scaling board
, be formulated as:
(1)
And for the blunt circle of milling cutter cutting edge: set cutting edge blunt round radius physical size value as L(is in units of millimeter mm), use the cutting edge blunt round radius Pixel Dimensions in the image of collected by camera to be that P(is in units of number of pixels), then its calibration coefficient
be formulated as follows:
(2)
When image acquisition, the parameter (as sighting distance, focal length, enlargement ratio) of camera lens and external condition (relative position of illumination, camera and target) are constant, the calibration coefficient of scaling board
equal the calibration coefficient of milling cutter cutting edge blunt round radius
.Then can be drawn by above two formula:
(3)
Wherein M is known dimensions 0.5mm, and P and N is obtained by software systems, thus draws the actual value of blunt round radius L, reaches the object of demarcation.
What select due to system is telecentric lens, this characteristic is not changed with the change of object distance according to the enlargement ratio of image in field range that telecentric lens has, as long as so when gathering image the parameter constant of camera and the clear picture (object is all in field range) of shooting, then the calibration coefficient of each image is all equal.Simultaneously the low distortion performance that also has of telecentric lens, greatly reduces the various distortions in image acquisition, increases the stated accuracy of system.
According to system calibrating principle, first IMAQLocalThresholdVI binaryzation scaling board image is used, secondly use the corresponding relation between the actual value of IMAQCalibrationTargettoPoints-CircularDotsVI acquisition scaling board distance of center circle and pixel value, obtain the calibration coefficient of scaling board
then use IMAQLearnDistortionModelVI with IMAQLearnPerspectiveCalibrationVI to correct due to the not exclusively vertical perspective distortion caused of camera shooting direction and scaling board image, finally use IMAQSetCalibrationInfoVI to demarcate tested milling cutter cutting edge image.
Image measurement module, creates a ROI(RegionOfInterest first in the picture) region, this region comprises the blunt circle contour of milling cutter cutting edge; Secondly, searched in ROI region by mode from inside to outside or from outside to inside, the lines in sector region are the direction of search, use the fixed-point calculation of bilinear interpolation or bilinear interpolation to determine marginal position; Finally use mean value method or intermediate value method that marginal point is fitted to circle, fitting circle radius value is approximately milling cutter blunt round radius and obtains measurement result.First use IMAQConstructROIVI to create circular arc ROI region in systems in which, then use the blunt round radius in IMAQFindCircularEdge3VI measurement ROI region.
Claims (5)
1., based on a milling cutter cutting edge blunt round radius detection system for machine vision, it comprises:
Image capture module, by CCD industrial camera and lens shooting scaling board and milling cutter cutting edge image, is converted to digital signal by image pick-up card by the simulating signal collected and is deposited into computing machine;
Image processing module, processes the image stored in computing machine, extracts milling cutter cutting edge edge contour;
System calibrating module, first demarcates scaling board image by binaryzation, obtains the corresponding relation between the actual value of scaling board distance of center circle and pixel value, obtain the calibration coefficient of scaling board
, then corrected perspective distortion, finally demarcates tested milling cutter cutting edge image;
Image measurement module, set up a ROI region, searched in ROI region by mode from inside to outside or from outside to inside, the fixed-point calculation of bilinear interpolation or bilinear interpolation is used to determine marginal position, finally use mean value method or intermediate value method that marginal point is fitted to circle, fitting circle radius value is milling cutter blunt round radius.
2. a kind of milling cutter cutting edge blunt round radius detection system based on machine vision according to claim 1, is characterized in that: image processing module comprises
Image enhaucament submodule, adopts histogram equalization and histogram specification to strengthen image;
Image filtering submodule, adopts medium filtering and low-pass filtering filtering image noise;
Iamge Segmentation submodule, adopting many threshold segmentation method and adaptive threshold split plot design segmentation image, is target area and background area two large regions by whole Iamge Segmentation;
Edge contour extracts submodule, uses Canny edge detection operator to extract milling cutter edge contour.
3. a kind of milling cutter cutting edge blunt round radius detection system based on machine vision according to claim 2, is characterized in that: the algorithm that adaptive threshold split plot design adopts is maximum variance between clusters and entropy principle method.
4. a kind of milling cutter cutting edge blunt round radius detection system based on machine vision according to claim 1, it is characterized in that: described scaling board is center of circle array calibrating plate, its physical dimension is 35mm × 35mm, round dot number is 50 × 50, circular diameter is 0.25mm, distance of center circle is 0.5mm, and manufacturing accuracy is 1 μm.
5. a kind of milling cutter cutting edge blunt round radius detection system based on machine vision according to claim 1, is characterized in that: the calibration coefficient of scaling board
computing formula be:
, in formula: M is the distance of center circle physical length of center of circle array calibrating plate, and unit is millimeter; N is the distance of center circle Pixel Dimensions in image, and unit is number of pixels.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105773309A (en) * | 2016-03-10 | 2016-07-20 | 天津大学 | Online testing method for ultra-view-field cutter |
| CN105865344A (en) * | 2016-06-13 | 2016-08-17 | 长春工业大学 | Workpiece dimension measuring method and device based on machine vision |
| CN106767467A (en) * | 2017-03-05 | 2017-05-31 | 贵州大学 | A kind of indexable insert tip, throw away tip blunt circle detecting system based on machine vision |
| CN107388991A (en) * | 2017-07-03 | 2017-11-24 | 中国计量大学 | A kind of more fillet axial workpiece radius of corner measuring methods in end face |
| CN108240793A (en) * | 2018-01-26 | 2018-07-03 | 广东美的智能机器人有限公司 | Dimension of object measuring method, device and system |
| CN108309708A (en) * | 2018-01-23 | 2018-07-24 | 李思霈 | Blind-man crutch |
| CN108581635A (en) * | 2018-05-03 | 2018-09-28 | 哈尔滨理工大学 | A kind of milling cutter side edge cutting edge abrasion three-dimensional detection device and method |
| CN109579715A (en) * | 2017-09-28 | 2019-04-05 | 宝山钢铁股份有限公司 | A kind of digitlization On-line Measuring Method improving hole expansibility measurement accuracy |
| CN110657750A (en) * | 2019-10-10 | 2020-01-07 | 西安多维机器视觉检测技术有限公司 | Detection system and method for passivation of cutting edge of cutter |
| CN111230593A (en) * | 2020-03-02 | 2020-06-05 | 西南交通大学 | Milling cutter abrasion loss visual measurement method based on dynamic image sequence |
| CN111275665A (en) * | 2019-12-19 | 2020-06-12 | 华中科技大学 | Blade grinding and polishing processing vibration detection system and method based on vision |
| CN111754490A (en) * | 2020-06-28 | 2020-10-09 | 湖南红太阳光电科技有限公司 | Graphite boat sheet-reversing detection method, device and system based on vision |
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| CN114878570A (en) * | 2022-05-18 | 2022-08-09 | 南京工业大学 | Indexable blade edge passivation detection system and method based on machine vision |
| PL445556A1 (en) * | 2020-12-31 | 2023-10-02 | Rosz Małgorzata | Calibration device |
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| CN105773309A (en) * | 2016-03-10 | 2016-07-20 | 天津大学 | Online testing method for ultra-view-field cutter |
| CN105865344A (en) * | 2016-06-13 | 2016-08-17 | 长春工业大学 | Workpiece dimension measuring method and device based on machine vision |
| CN106767467A (en) * | 2017-03-05 | 2017-05-31 | 贵州大学 | A kind of indexable insert tip, throw away tip blunt circle detecting system based on machine vision |
| CN107388991A (en) * | 2017-07-03 | 2017-11-24 | 中国计量大学 | A kind of more fillet axial workpiece radius of corner measuring methods in end face |
| CN107388991B (en) * | 2017-07-03 | 2019-12-03 | 中国计量大学 | A kind of more fillet axial workpiece radius of corner measurement methods in end face |
| CN109579715A (en) * | 2017-09-28 | 2019-04-05 | 宝山钢铁股份有限公司 | A kind of digitlization On-line Measuring Method improving hole expansibility measurement accuracy |
| CN108309708A (en) * | 2018-01-23 | 2018-07-24 | 李思霈 | Blind-man crutch |
| CN108240793A (en) * | 2018-01-26 | 2018-07-03 | 广东美的智能机器人有限公司 | Dimension of object measuring method, device and system |
| CN108581635B (en) * | 2018-05-03 | 2023-10-13 | 哈尔滨理工大学 | Three-dimensional detection device and method for abrasion of side edge of milling cutter |
| CN108581635A (en) * | 2018-05-03 | 2018-09-28 | 哈尔滨理工大学 | A kind of milling cutter side edge cutting edge abrasion three-dimensional detection device and method |
| CN110657750A (en) * | 2019-10-10 | 2020-01-07 | 西安多维机器视觉检测技术有限公司 | Detection system and method for passivation of cutting edge of cutter |
| CN111275665A (en) * | 2019-12-19 | 2020-06-12 | 华中科技大学 | Blade grinding and polishing processing vibration detection system and method based on vision |
| CN111230593A (en) * | 2020-03-02 | 2020-06-05 | 西南交通大学 | Milling cutter abrasion loss visual measurement method based on dynamic image sequence |
| CN111754490A (en) * | 2020-06-28 | 2020-10-09 | 湖南红太阳光电科技有限公司 | Graphite boat sheet-reversing detection method, device and system based on vision |
| CN111754490B (en) * | 2020-06-28 | 2023-08-08 | 湖南红太阳光电科技有限公司 | Graphite boat film rewinding detection method, device and system based on vision |
| CN111798524A (en) * | 2020-07-14 | 2020-10-20 | 华侨大学 | Calibration system and method based on inverted low-resolution camera |
| CN111798524B (en) * | 2020-07-14 | 2023-07-21 | 华侨大学 | Calibration system and method based on inverted low-resolution camera |
| PL445556A1 (en) * | 2020-12-31 | 2023-10-02 | Rosz Małgorzata | Calibration device |
| CN114878570A (en) * | 2022-05-18 | 2022-08-09 | 南京工业大学 | Indexable blade edge passivation detection system and method based on machine vision |
| CN114878570B (en) * | 2022-05-18 | 2023-09-08 | 南京工业大学 | Indexable insert cutting edge passivation detection method based on machine vision |
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