CN109035236B - Casting burr detection method and device - Google Patents
Casting burr detection method and device Download PDFInfo
- Publication number
- CN109035236B CN109035236B CN201810846614.XA CN201810846614A CN109035236B CN 109035236 B CN109035236 B CN 109035236B CN 201810846614 A CN201810846614 A CN 201810846614A CN 109035236 B CN109035236 B CN 109035236B
- Authority
- CN
- China
- Prior art keywords
- casting
- image
- detected
- burrs
- reference image
- 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
Links
- 238000005266 casting Methods 0.000 title claims abstract description 248
- 238000001514 detection method Methods 0.000 title claims abstract description 53
- 238000012545 processing Methods 0.000 claims abstract description 29
- 238000004364 calculation method Methods 0.000 claims abstract description 27
- 238000000034 method Methods 0.000 claims abstract description 27
- 238000003709 image segmentation Methods 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 2
- 238000003708 edge detection Methods 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 abstract description 11
- 238000005286 illumination Methods 0.000 description 15
- 230000006870 function Effects 0.000 description 8
- 230000011218 segmentation Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 239000003990 capacitor Substances 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 239000002184 metal Substances 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 238000005520 cutting process Methods 0.000 description 2
- 238000010191 image analysis Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000000465 moulding Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20036—Morphological image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
Abstract
The casting burr detection method and device comprise the following steps: acquiring an original image of the detected casting acquired by an image acquisition device under a preset condition, wherein the preset condition is that a diffuse reflection light source is adopted and the detected object is irradiated from the rear of the detected object; dividing the background in the original image and the detected casting to obtain a casting image; performing open operation calculation on the casting image to obtain a reference image; and carrying out difference processing on the casting image and the reference image to determine whether the casting to be detected has burrs or not. The embodiment of the invention meets the intelligent requirement on the detection of the product process in an automatic manufacturing system, and improves the detection efficiency and the reliability of the detection result.
Description
Technical Field
The invention relates to the field of die manufacturing, in particular to a casting burr detection method and device.
Background
At present, a large part of metal and plastic products inevitably have certain flaws such as marks, erosion, scratches, burrs and the like in the production process due to objective technological processes. Particularly in the case of impossible complete closure with the grinding tool, burrs are produced, which seriously affect the quality of the workpiece. Therefore, in the production process of molded products, the detection of burrs is very important, and is an important link for ensuring the quality of products.
The existing production process also uses an artificial traditional method for detecting the burrs of the castings, relies on experience and visual observation to identify the burrs, has certain subjectivity and uncertainty, has the conditions of missed detection and false detection, leads to low detection efficiency and consumes a large amount of manpower.
Disclosure of Invention
In view of the above, it is necessary to provide a method and apparatus for detecting burrs of castings, which solve the problem of low efficiency of detecting burrs of castings in the prior art.
A casting burr detection method, comprising:
acquiring an original image of the detected casting acquired by an image acquisition device under a preset condition, wherein the preset condition is that a diffuse reflection light source is adopted and the detected object is irradiated from the rear of the detected object;
dividing the background in the original image and the detected casting to obtain a casting image;
performing open operation calculation on the casting image to obtain a reference image;
and carrying out difference processing on the casting image and the reference image to determine whether the casting to be detected has burrs or not.
Further, in the casting burr detection method, the step of performing difference processing on the casting image and the reference image to determine whether the detected casting has burrs includes:
performing difference processing on the casting image and the reference image to obtain the outline of the casting to be detected;
calculating the distance between each contour point on the contour of the casting to be measured and the boundary of the reference image to obtain a plurality of distance values;
and when any distance value is larger than a threshold distance, determining that burrs exist on the detected casting.
Further, in the casting burr detection method, the step of performing open operation calculation on the casting image includes:
and taking a circle smaller than the radius of the casting to be measured as a basic unit of open operation, and performing open operation calculation on the casting image.
Further, the casting burr detection method, wherein after the step of determining whether the detected casting has burrs, further comprises:
when the detected casting has burrs, determining a starting point and an ending point of the area where the burrs are positioned;
and respectively calculating the angles of the starting point and the ending point relative to the center of the casting to be measured.
Further, in the casting burr detection method, the step of determining the starting point and the end point of the area where the burrs are located includes:
expanding the region outline of the reference image outwards by one pixel to obtain the boundary of the reference image;
and determining two end points of the intersection of the area where the burrs are positioned and the boundary, and respectively serving as a starting point and an end point of the area where the burrs are positioned.
Further, in the casting burr detection method, the step of dividing the background in the original image and the casting to be detected to obtain the casting image includes:
and segmenting the background in the original image and the detected casting by adopting an LBF model to obtain a casting image.
The embodiment of the invention also provides a casting burr detection device, which comprises an image acquisition device, an illumination device and an image processor, wherein the illumination device is arranged on the back surface of the detected casting area, and the image processor comprises:
the acquisition module is used for acquiring an original image of the detected casting acquired by the image acquisition device under a preset condition, wherein the preset condition is that a diffuse reflection light source is adopted and the detected object is irradiated from the rear of the detected object;
the image segmentation module is used for segmenting the background in the original image and the detected casting to obtain a casting image;
the first calculation module is used for performing open operation calculation on the casting image to obtain a reference image;
and the processing module is used for carrying out difference processing on the casting image and the reference image so as to determine whether the casting to be detected has burrs or not.
Further, the casting burr detection device, wherein the processing module is specifically configured to:
performing difference processing on the casting image and the reference image to obtain the outline of the casting to be detected;
calculating the distance between each contour point on the contour of the casting to be measured and the boundary of the reference image to obtain a plurality of distance values;
and when any distance value is larger than a threshold distance, determining that burrs exist on the detected casting.
Further, the casting burr detection device, wherein the first calculation module is specifically configured to:
and taking a circle smaller than the radius of the casting to be measured as a basic unit of open operation, and performing open operation calculation on the casting image.
Further, the casting burr detection device further comprises:
the determining module is used for determining the starting point and the end point of the area where the burrs are positioned when the detected casting has the burrs;
and the second calculation module is used for calculating the angles of the starting point and the end point relative to the center of the casting to be measured respectively.
Further, the casting burr detection device, wherein the determining module is specifically configured to:
expanding the region outline of the reference image outwards by one pixel to obtain the boundary of the reference image;
and determining two end points of the intersection of the area where the burrs are positioned and the boundary, and respectively serving as a starting point and an end point of the area where the burrs are positioned.
Further, the casting burr detection device, wherein the image segmentation module is specifically configured to:
and segmenting the background in the original image and the detected casting by adopting an LBF model to obtain a casting image.
According to the embodiment of the invention, the original image of the casting to be detected is acquired in a bright field diffuse reflection back illumination mode, so that the object can be extracted from the acquired casting image, the background is highlighted, the obvious object of the casting is black, and the subsequent cutting burr speed can be greatly improved. The method of image segmentation is adopted to segment the detected casting and the background, the original image is processed through open operation in morphology, the image after the open operation processing is subtracted from the casting image, the image of the edge contour of the casting can be accurately segmented, and the distance calculation is carried out on the edge contour of the casting, so that whether burrs exist on the detected casting can be accurately judged. The embodiment of the invention meets the intelligent requirement on the detection of the product process in an automatic manufacturing system, and improves the detection efficiency and the reliability of the detection result.
Drawings
FIG. 1 is a flow chart of a casting burr detection method in a first embodiment of the invention;
FIG. 2 is a diagram of the positional relationship of the image capturing device and the illumination device;
FIG. 3 is a flow chart of a casting burr detection method in a second embodiment of the invention;
FIG. 4 is a schematic illustration of an image of a casting obtained after image segmentation in a second embodiment of the present invention;
FIG. 5 is a schematic representation of an image of the contours of a casting being inspected in a second embodiment of the present invention;
FIG. 6 is a schematic view showing the positions of burrs in a second embodiment of the invention.
FIG. 7 is a block diagram showing the construction of a casting burr detecting apparatus in a third embodiment of the invention;
fig. 8 is a block diagram showing the structure of the image processor in the third embodiment of the present invention;
fig. 9 is a schematic circuit diagram of a warning device according to a third embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
These and other aspects of embodiments of the invention will be apparent from and elucidated with reference to the description and drawings described hereinafter. In the description and drawings, particular implementations of embodiments of the invention are disclosed in detail as being indicative of some of the ways in which the principles of embodiments of the invention may be employed, but it is understood that the scope of the embodiments of the invention is not limited correspondingly. On the contrary, the embodiments of the invention include all alternatives, modifications and equivalents as may be included within the spirit and scope of the appended claims.
Referring to fig. 1, a method for detecting burrs of a casting according to a first embodiment of the present invention is applied to a device for detecting burrs of a casting, and the device for detecting burrs of a casting includes an image acquisition device, an illumination device and an image processor. The image acquisition device is used for adopting the image of the casting, and can adopt a CMOS image acquisition device. The illumination device is used for providing a light source in the casting detection process so as to obtain a clear image of the casting to be detected. In practice, as shown in FIG. 2, the illumination device is mounted on the back of the area of the casting being inspected, i.e., illuminated from the back of the inspected casting, to highlight the casting contours. The illumination device adopts a diffuse reflection light source, and can effectively extract the salient region of the casting by adopting a bright field diffuse backlight illumination structure. The image processor is used to analyze and process the acquired images, and may be, for example, a computer device or a dedicated image processor. The casting burr detection method comprises steps S11 to S14.
Step S11, acquiring an original image of the detected casting acquired by the image acquisition device under a preset condition, wherein the preset condition is that a diffuse reflection light source is adopted and the detected object is irradiated from the rear of the detected object.
The steps of the method can be executed by an image processor of the casting burr detection device, and the image processor acquires the original image of the casting to be detected, which is acquired by the image acquisition device under the preset condition. The preset condition is that the casting is irradiated from the back of the casting to be tested by adopting a diffuse reflection lighting device. The background of the image of the detected casting, which is acquired through diffuse reflection and bright view field backlight, is bright, and the target is darker, so that the subsequent image segmentation speed can be greatly improved.
And step S12, segmenting the background in the original image and the detected casting to obtain a casting image.
And dividing an original image of the casting to be detected by an image dividing method, and separating objects in the original image from the background. There are various conventional image segmentation algorithm methods, such as a threshold-based segmentation method, an edge-based segmentation method, a region-based segmentation method, and a clustering-analysis-based segmentation method. In this embodiment, a LBF (Local Binary Fitting) model is used to segment the background and the object. The LBF model is a region-based active contour model, and the algorithm uses a gaussian function to obtain the gray information of an image, without local gray threshold segmentation, and the gray can be approximately regarded as that no bias field exists in the global. When the LBF model is subjected to integral segmentation, the calculated amount is small, the evolution convergence is slow and quick, and the efficiency is high.
And step S13, performing open operation calculation on the casting image to obtain a reference image.
And step S14, carrying out difference processing on the casting image and the reference image to determine whether burrs exist on the tested casting.
In order to determine whether or not burrs exist at the edges of the casting, an edge image of the casting needs to be obtained for analysis of the edge image. In this embodiment, the regional morphology is used to detect the regional burrs, and the burrs at the edge of the common cast sword are presented as convex parts, and the open operation result is subtracted from the original image by using the open operation of the regional morphology. And when the casting image is subjected to open operation, determining a proper open operation basic structural unit according to the shape and the size of the casting. The round shape of the casting in this embodiment is therefore used as the basic structural unit. The radius of the circle is slightly smaller than the radius of the casting, for example, the radius of the circle is slightly smaller than the radius of the casting by 0.01mm, so that the edge burrs of the casting are removed after the operation is started, and the whole casting can be prevented from being removed.
Furthermore, on the basis of the above open operation, a window of 5*5 can be used as a structural element to perform one-time open operation calculation, so that small burrs are further removed. And performing open operation calculation on the casting image for a plurality of times to obtain a reference image in which the outline of the casting is smooth and clear.
And performing difference processing on the casting image and the reference image, namely subtracting the calculated area from the object area in the original image, so as to divide burrs. And determining whether the casting to be tested has burrs or not according to the image processed by the difference value. Specifically, the specific steps for determining whether the detected casting has burrs or not include:
performing difference processing on the casting image and the reference image to obtain the outline of the casting to be detected;
calculating the distance between each contour point on the contour of the casting to be measured and the boundary of the reference image to obtain a plurality of distance values;
and when any distance value is larger than a threshold distance, determining that burrs exist on the detected casting.
And performing difference processing on the casting image and the reference image to obtain the outline of the casting to be detected. The distance between each contour point on the contour and the boundary of the reference image, namely the vertical distance, is calculated. Here, the calculated distance is obtained by performing a distance transformation on the region of the open operation with the measured object after the open operation being expected as a reference shape. And when any contour point on the contour of the detected casting is larger than a preset threshold distance, determining that burrs exist on the detected casting. The preset distance can be set according to actual needs, for example, set to 0.1mm.
According to the embodiment, the original image of the casting to be detected is obtained in a bright field diffuse reflection back illumination mode, so that the object can be extracted from the collected casting image, background highlighting is achieved, the obvious object of the casting is black, and the subsequent cutting burr speed can be greatly improved. The method of image segmentation is adopted to segment the detected casting and the background, the original image is processed through open operation in morphology, the image after the open operation processing is subtracted from the casting image, the image of the edge contour of the casting can be accurately segmented, and the distance calculation is carried out on the edge contour of the casting, so that whether burrs exist on the detected casting can be accurately judged. The embodiment meets the intelligent requirement on the detection of the product process in the automatic manufacturing system, and improves the detection efficiency and the reliability of the detection result.
Referring to fig. 3, a method for detecting burrs of castings according to a second embodiment of the present invention includes steps S21 to S26.
Step S21, acquiring an original image of the detected casting acquired by the image acquisition device under a preset condition, wherein the preset condition is that a diffuse reflection light source is adopted and the detected object is irradiated from the rear of the detected object.
The casting is irradiated from the back of the casting to be detected by adopting a diffuse reflection lighting device, the background of the image of the casting to be detected, which is acquired by diffuse reflection and bright field backlight, is bright, the target is darker, and the subsequent image segmentation speed can be greatly improved.
And S22, segmenting the background in the original image and the detected casting by adopting an LBF model to obtain a casting image.
The LBF model is a region-based active contour model, which takes the gray information of an image using a gaussian function and determines the range of the fitted gray value region by the magnitude of the parameters of the non-negative kernel function. And (3) performing image segmentation on the original image to obtain a casting image (shown in fig. 4) with the background removed.
And S23, performing open operation calculation on the casting image to obtain a reference image.
And step S24, carrying out difference processing on the casting image and the reference image to obtain the outline of the tested casting.
In the embodiment, the casting image is processed by adopting open operation in regional morphology to obtain a reference image. And performing difference processing on the casting image and the reference image to obtain an image (shown in fig. 5) of the outline of the casting to be detected, wherein the outline image clearly shows the edge condition of the casting. The open operation adopts a circle as a basic structural unit, the size of the circle is slightly smaller than that of the casting, so that burrs on the casting can be removed, and the whole object can not be removed. To ensure that small burr areas on the boundary can also be removed, a window of 5*5 is used here as a structural element to further remove the small burrs.
And S25, calculating the distance between each contour point on the contour of the casting to be detected and the boundary of the reference image to obtain a plurality of distance values.
And S26, when any distance value is larger than a threshold distance, determining that burrs exist on the detected casting, and determining the starting point and the end point of the area where the burrs are located.
And calculating the distance between each contour point on the contour of the casting to be measured and the boundary of the reference image, wherein the distance refers to the vertical distance. Here, the calculated distance is obtained by performing a distance transformation on the region of the open operation with the measured object after the open operation being expected as a reference shape. And when any contour point on the contour of the detected casting is larger than a preset threshold distance, determining that burrs exist on the detected casting. When it is determined that there is a burr on the casting, the starting point and the ending point of the area where the burr is located are detected.
Specifically, the step of determining the starting point and the end point of the region where the burrs are located includes:
expanding the region outline of the reference image outwards by one pixel to obtain the boundary of the reference image;
and determining two end points of the intersection of the area where the burrs are positioned and the boundary, and respectively serving as a starting point and an end point of the area where the burrs are positioned.
The boundary of the reference image is obtained by expanding the position of one pixel outwards on the original area, each communication part of the burr area is intersected with the boundary, the end point containing the intersected area can be divided by a skeleton operator, and two end points can be determined for any burr area, namely the starting point and the end point of the burr.
Through the distance transformation in morphology and the skeleton frame, the size information of the burrs can be accurately obtained, and the gray morphology can be reliably used for detecting small burrs.
And step S27, calculating angles of the starting point and the end point relative to the center of the casting to be measured respectively.
The present embodiment can use the arctangent function to calculate the angles of the starting point and the ending point of the burr relative to the center of the casting being measured. As shown in fig. 6, the angle of the start point and the end point of one of the burrs.
It will be appreciated that in this embodiment, the number of burrs on the casting detected is variable, and may be one or more, and when there are multiple burrs, the starting and ending positions of the area where each burr is located need to be determined. The positions of the start point and the end point of each burr are confirmed in the same manner.
Further, as an implementation mode of the invention, when the burrs on the detected casting are determined, an alarm prompt can be sent out. The alarm prompt is realized by triggering an alarm device, and the alarm device comprises a buzzer and an LED alarm lamp.
The casting burr detector in the embodiment can be used in various occasions, particularly in the production process of metal and plastic castings, sharp burrs generated at the edges of the casting burr detector can accurately give out the size and position information of burrs, the burr removing treatment in the later stage is convenient, and the casting burr detector has important significance in the aspect of casting quality detection. The intelligent detection method and the intelligent detection device meet the intellectualization of product process detection in an automatic manufacturing system, and improve the detection efficiency and the reliability of detection results.
Referring to fig. 7, a casting burr detecting apparatus according to a third embodiment of the invention includes an image processor 10, and an image acquisition device 20, an illumination device 30 and an alarm device 40 connected to the image processor 10.
The illumination device is used for providing a light source in the casting detection process so as to obtain a clear image of the casting to be detected. In particular, the illumination device 30 is mounted on the back of the area of the casting being inspected, i.e., illuminated from the back of the casting being inspected, to highlight the casting contours. The illumination device 30 adopts a diffuse reflection light source, namely adopts a bright field diffuse backlight illumination structure, and can effectively extract the significant areas of castings.
The image capture device 20 is used for images using castings, which may be a CMOS image capture device. The original image of the casting to be measured acquired by the image acquisition device 20 is sent to an image processor for image analysis and processing to determine whether there is a burr on the edge of the casting to be measured, and the size and position of the burr. Specifically, the image processor as shown in fig. 8 includes:
the acquisition module 101 is configured to acquire an original image of the casting to be detected, which is acquired by the image acquisition device under a preset condition, where the preset condition is that a diffuse reflection light source is adopted and the object to be detected is irradiated from the back of the object to be detected;
the image segmentation module 102 is used for segmenting the background in the original image and the casting to be detected to obtain a casting image;
a first calculation module 103, configured to perform an open operation calculation on the casting image to obtain a reference image;
and the processing module 104 is used for carrying out difference processing on the casting image and the reference image so as to determine whether the casting to be detected has burrs or not.
Further, in the casting burr detecting apparatus, the processing module 104 is specifically configured to:
performing difference processing on the casting image and the reference image to obtain the outline of the casting to be detected;
calculating the distance between each contour point on the contour of the casting to be measured and the boundary of the reference image to obtain a plurality of distance values;
and when any distance value is larger than a threshold distance, determining that burrs exist on the detected casting.
Further, in the casting burr detecting apparatus, the first calculating module 103 is specifically configured to:
and taking a circle smaller than the radius of the casting to be measured as a basic unit of open operation, and performing open operation calculation on the casting image.
Further, the casting burr detection device, the image processor further comprises:
a determining module 105, configured to determine a start point and an end point of an area where the burrs are located when the detected casting has the burrs;
a second calculation module 106 for calculating the angles of the start point and the end point, respectively, relative to the center of the casting under test.
Further, in the casting burr detecting apparatus, the determining module 105 is specifically configured to:
expanding the region outline of the reference image outwards by one pixel to obtain the boundary of the reference image;
and determining two end points of the intersection of the area where the burrs are positioned and the boundary, and respectively serving as a starting point and an end point of the area where the burrs are positioned.
Further, in the casting burr detection apparatus, the image segmentation module 102 is specifically configured to:
and segmenting the background in the original image and the detected casting by adopting an LBF model to obtain a casting image.
As shown in fig. 9, the alarm device 40 includes a control circuit 41, a buzzer 41 and an LED alarm lamp 42 connected to the control circuit, wherein the control circuit includes a monostable trigger 43, and a triode Q1 connected to the monostable trigger 43. The monostable trigger 43 may be, for example, a 74LS122 chip, where the positive pulse output end Q is connected to the base of a triode Q1 through a resistor, the collector of the triode Q1 is connected to the buzzer 41, the emitter is connected to the LED alarm lamp 42, and the LED alarm lamp 42 is grounded. The external capacitor terminal CX of the monostable trigger 43 is connected to a capacitor C1, the capacitor C1 is connected to a first resistor R1, the other end of the first resistor R1 is connected to one end of a second resistor R2, and the other end of the second resistor R2 is connected to the buzzer 41. The positive trigger input B1 of the monostable is connected to the image processor 10. And the image processor outputs corresponding electric signals to control the buzzer and the LED alarm lamp to work according to the image analysis result of the casting to be detected.
Specifically, when the image processor analyzes that the detected casting has burrs, the monostable trigger 43 triggered again converts the continuous pulse signal into high level, and under the high level trigger of the signal output by the output end Q, the triode is excited to drive the buzzer 41 and the LED alarm lamp 42 to work. It should be noted that the signal pulse at the output of the monostable 43 needs to be greater than the integration period of the acquisition signal.
Further, the casting burr detecting apparatus further includes a display screen 50 and a control panel 60 connected to the image processor 10. The control panel 60 is provided with a plurality of keys for controlling the functions of the casting burr detecting device, respectively. For example, the casting burr detecting device is controlled to be opened and closed, and the resetting and outputting modes of the casting burr detecting device are controlled.
The display 50 is connected to the image processor 10, and is configured to receive the burr parameterized information, such as size and position, sent by the image processor 10, and dynamically display the information sent by the image processor 10 in real time, so as to facilitate the user to view.
The casting burr detection device in the embodiment can be used in various occasions, particularly in the production process of metal and plastic castings, sharp burrs generated at the edges of the casting burr detection device can accurately give out the size and position information of burrs, the burr removal processing in the later stage is convenient, and the casting burr detection device has important significance in the aspect of casting quality detection. Meanwhile, the casting burr detection device in the embodiment is low in consumable material price and molding cost, and can be widely popularized in quality detection links in casting production procedures.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (8)
1. A casting burr detection method, characterized by comprising:
acquiring an original image of the detected casting acquired by an image acquisition device under a preset condition, wherein the preset condition is that a diffuse reflection light source is adopted and the detected object is irradiated from the rear of the detected object;
dividing the background in the original image and the detected casting to obtain a casting image;
performing open operation calculation on the casting image to obtain a reference image;
performing difference processing on the casting image and the reference image to determine whether the casting to be detected has burrs or not;
the step of performing difference processing on the casting image and the reference image to determine whether the casting to be detected has burrs or not comprises the following steps:
performing difference processing on the casting image and the reference image to obtain the outline of the casting to be detected;
calculating the distance between each contour point on the contour of the casting to be measured and the boundary of the reference image to obtain a plurality of distance values;
and when any distance value is larger than a threshold distance, determining that burrs exist on the detected casting.
2. The casting burr detection method according to claim 1, wherein the step of performing an open operation calculation on the casting image includes:
and taking a circle smaller than the radius of the casting to be measured as a basic unit of open operation, and performing open operation calculation on the casting image.
3. The method of detecting flash in castings according to claim 1, wherein said step of determining whether said castings under test have flash further comprises:
when the detected casting has burrs, determining a starting point and an ending point of the area where the burrs are positioned;
and respectively calculating the angles of the starting point and the ending point relative to the center of the casting to be measured.
4. The method of detecting burrs of a casting as defined in claim 3, wherein said step of determining the starting and ending points of the area of said burrs comprises:
expanding the region outline of the reference image outwards by one pixel to obtain the boundary of the reference image;
and determining two end points of the intersection of the area where the burrs are positioned and the boundary, and respectively serving as a starting point and an end point of the area where the burrs are positioned.
5. The method of claim 1, wherein the step of segmenting the background in the original image and the casting to be tested to obtain an image of the casting comprises:
and segmenting the background in the original image and the detected casting by adopting an LBF model to obtain a casting image.
6. The utility model provides a foundry goods deckle edge detection device, its characterized in that includes image acquisition device, lighting device and image processor, lighting device installs the back in the foundry goods area of survey, image processor includes:
the acquisition module is used for acquiring an original image of the detected casting acquired by the image acquisition device under a preset condition, wherein the preset condition is that a diffuse reflection light source is adopted and the detected object is irradiated from the rear of the detected object;
the image segmentation module is used for segmenting the background in the original image and the detected casting to obtain a casting image;
the first calculation module is used for performing open operation calculation on the casting image to obtain a reference image;
the processing module is used for carrying out difference processing on the casting image and the reference image so as to determine whether the casting to be detected has burrs or not;
the processing module is specifically configured to:
performing difference processing on the casting image and the reference image to obtain the outline of the casting to be detected;
calculating the distance between each contour point on the contour of the casting to be measured and the boundary of the reference image to obtain a plurality of distance values;
and when any distance value is larger than a threshold distance, determining that burrs exist on the detected casting.
7. The casting burr detection apparatus of claim 6, wherein the first calculation module is specifically configured to:
and taking a circle smaller than the radius of the casting to be measured as a basic unit of open operation, and performing open operation calculation on the casting image.
8. The casting burr detection apparatus according to claim 6, further comprising:
the determining module is used for determining the starting point and the end point of the area where the burrs are positioned when the detected casting has the burrs;
and the second calculation module is used for calculating the angles of the starting point and the end point relative to the center of the casting to be measured respectively.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810846614.XA CN109035236B (en) | 2018-07-27 | 2018-07-27 | Casting burr detection method and device |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810846614.XA CN109035236B (en) | 2018-07-27 | 2018-07-27 | Casting burr detection method and device |
Publications (2)
Publication Number | Publication Date |
---|---|
CN109035236A CN109035236A (en) | 2018-12-18 |
CN109035236B true CN109035236B (en) | 2024-02-23 |
Family
ID=64646249
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810846614.XA Active CN109035236B (en) | 2018-07-27 | 2018-07-27 | Casting burr detection method and device |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109035236B (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114387438B (en) * | 2022-03-23 | 2022-06-10 | 武汉锦辉压铸有限公司 | Machine vision-based die casting machine parameter regulation and control method |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102080786A (en) * | 2010-10-18 | 2011-06-01 | 丰铁流 | Multi-pyramid light emitting diode (LED) diffuse reflection backlight source |
CN203750868U (en) * | 2013-11-20 | 2014-08-06 | 南京信息工程大学 | Computer vision-based burr detection device |
CN104075659A (en) * | 2014-06-24 | 2014-10-01 | 华南理工大学 | Three-dimensional imaging recognition method based on RGB structure light source |
CN104981105A (en) * | 2015-07-09 | 2015-10-14 | 广东工业大学 | Detecting and error-correcting method capable of rapidly and accurately obtaining element center and deflection angle |
CN106017328A (en) * | 2015-12-17 | 2016-10-12 | 广东正业科技股份有限公司 | A multi-type line width measurement method and device |
CN106296636A (en) * | 2015-06-02 | 2017-01-04 | 征图新视(江苏)科技有限公司 | The detection method of printing image and detection device |
CN106404793A (en) * | 2016-09-06 | 2017-02-15 | 中国科学院自动化研究所 | Method for detecting defects of bearing sealing element based on vision |
WO2017032308A1 (en) * | 2015-08-25 | 2017-03-02 | 广州视源电子科技股份有限公司 | Pcb board detection method and apparatus |
CN107808378A (en) * | 2017-11-20 | 2018-03-16 | 浙江大学 | Complicated structure casting latent defect detection method based on vertical co-ordination contour feature |
-
2018
- 2018-07-27 CN CN201810846614.XA patent/CN109035236B/en active Active
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102080786A (en) * | 2010-10-18 | 2011-06-01 | 丰铁流 | Multi-pyramid light emitting diode (LED) diffuse reflection backlight source |
CN203750868U (en) * | 2013-11-20 | 2014-08-06 | 南京信息工程大学 | Computer vision-based burr detection device |
CN104075659A (en) * | 2014-06-24 | 2014-10-01 | 华南理工大学 | Three-dimensional imaging recognition method based on RGB structure light source |
CN106296636A (en) * | 2015-06-02 | 2017-01-04 | 征图新视(江苏)科技有限公司 | The detection method of printing image and detection device |
CN104981105A (en) * | 2015-07-09 | 2015-10-14 | 广东工业大学 | Detecting and error-correcting method capable of rapidly and accurately obtaining element center and deflection angle |
WO2017032308A1 (en) * | 2015-08-25 | 2017-03-02 | 广州视源电子科技股份有限公司 | Pcb board detection method and apparatus |
CN106017328A (en) * | 2015-12-17 | 2016-10-12 | 广东正业科技股份有限公司 | A multi-type line width measurement method and device |
CN106404793A (en) * | 2016-09-06 | 2017-02-15 | 中国科学院自动化研究所 | Method for detecting defects of bearing sealing element based on vision |
CN107808378A (en) * | 2017-11-20 | 2018-03-16 | 浙江大学 | Complicated structure casting latent defect detection method based on vertical co-ordination contour feature |
Non-Patent Citations (2)
Title |
---|
李冬.基于FPGA的金属毛刺视觉检测系统研究与实现.《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》.2017,B022-1345. * |
郭杰 ; 雷刚 ; 陈健生 ; 向守兵 ; .基于机器视觉的钢包头检测系统设计.液晶与显示.2013,(05),正文第4页第3.3部分. * |
Also Published As
Publication number | Publication date |
---|---|
CN109035236A (en) | 2018-12-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Nienaber et al. | Detecting potholes using simple image processing techniques and real-world footage | |
CN109242853B (en) | An intelligent detection method for PCB defects based on image processing | |
CN112577969B (en) | Defect detection method and defect detection system based on machine vision | |
CN107895362B (en) | A machine vision method for quality inspection of miniature terminals | |
WO2023039781A1 (en) | Method for detecting abandoned object, apparatus, electronic device, and storage medium | |
CN107798688B (en) | Moving target identification method, early warning method and automobile rear-end collision prevention early warning device | |
CN114255202A (en) | Method, apparatus, system and storage medium for detecting holes in a workpiece | |
CN111122590A (en) | Ceramic surface defect detection device and detection method | |
CN107345916B (en) | Plane appearance detection method based on fixed contour | |
CN113139943B (en) | Method and system for detecting appearance defects of open circular ring workpiece and computer storage medium | |
CN108802051A (en) | A kind of flexibility IC substrate straight path bubbles and folding line defect detecting system and method | |
CN113793322A (en) | Method for automatically detecting magnetic material, electronic equipment and storage medium | |
EP3510526A1 (en) | Particle boundary identification | |
CN109035236B (en) | Casting burr detection method and device | |
CN117036259A (en) | Metal plate surface defect detection method based on deep learning | |
KR100687811B1 (en) | Welding defect detection method and device | |
CN108550142A (en) | A kind of tooth hole inspection method and hole inspection and device | |
EP2756450A1 (en) | Method for detection of a rain drop on the windscreen of a vehicle and driver assistance system | |
CN118735861A (en) | High-precision detection method and system for component defects based on machine vision | |
CN114913112A (en) | Method, device and equipment for detecting double edges of wafer | |
CN113888539B (en) | Defect classification method, device, equipment and storage medium | |
CN107833222A (en) | A kind of non-metal workpiece fifth wheel detection means and method | |
CN112129700B (en) | Image detection method and device for flexible circuit board | |
CN116258703A (en) | Defect detection method, defect detection device, electronic equipment and computer readable storage medium | |
CN113763491A (en) | Visual detection method for tobacco shred barrel residues |
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 |