CN209736098U - Flexible detection and analysis system for major surface and internal defects of casting - Google Patents
Flexible detection and analysis system for major surface and internal defects of casting Download PDFInfo
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- CN209736098U CN209736098U CN201920324364.3U CN201920324364U CN209736098U CN 209736098 U CN209736098 U CN 209736098U CN 201920324364 U CN201920324364 U CN 201920324364U CN 209736098 U CN209736098 U CN 209736098U
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Abstract
The utility model belongs to the technical field of intelligent identification and detection of castings, specifically disclose a flexible detection and analysis system of key surface and internal defect of casting, including hardware system and control system, its characterized in that, hardware system includes the conveyer belt of band edge frame, is provided with X ray detection storehouse on the conveyer belt frame, and X ray detection storehouse one side is provided with robotic arm, and the opposite side is provided with the detection support, is provided with CCD camera and annular light source on the detection support, and CCD camera and robotic arm all are connected with computer terminal through the network cable, are provided with position sensor on the conveyer belt frame below the annular light source; the other side of the mechanical arm is provided with a qualified product outlet; a defective product box is arranged right in front of the mechanical arm, is arranged on one side of the conveyor belt and has a gap with the conveyor belt; the mechanical arm is fixedly connected with the frame of the conveyor belt; the detection bracket is fixedly connected with the conveyor belt frame; the CCD camera is arranged above the axis of the annular light source.
Description
Technical Field
the utility model belongs to the technical field of foundry goods intelligent recognition and detection, concretely relates to flexible detection and analysis system of major surface of foundry goods and internal defect.
Background
In the casting production process, due to the design of a pouring system, the surface and the interior of a casting inevitably have defects of cracks, shrinkage cavities, shrinkage porosity and the like; the quality of the casting affects the quality safety and the service life of parts and even the whole mechanical equipment, so the quality detection of the casting is an important link in the production process; at present, domestic casting enterprises mostly adopt manual visual detection to detect the defects of castings, and rely on human eye identification and judgment of workers, and the traditional detection method has strong subjectivity, high labor intensity, low efficiency and poor precision; as an important technology in the current industry, the machine vision inspection technology is widely applied to various fields such as content-based image retrieval, automobile safety, video monitoring, and robotics, and along with the development of the technology, the machine vision inspection technology is applied to more and more fields to meet the ever-increasing market demand.
SUMMERY OF THE UTILITY MODEL
the problem that artifical subjective consciousness is strong, inefficiency, poor and productivity ratio is low when overcoming the key surface and the internal defect that detect the foundry goods, the utility model provides an improve foundry goods product quality detection efficiency and detect the foundry goods key surface of precision and internal defect's flexible detection analytic system.
Based on the above-mentioned purpose, the utility model discloses a following technical scheme realizes:
A flexible detection and analysis system for major surface and internal defects of a casting comprises a hardware system and a control system, wherein the hardware system comprises a conveyor belt with a frame, an X-ray detection bin is arranged on the frame of the conveyor belt, a mechanical arm is arranged on one side of the X-ray detection bin, a detection support is arranged on the other side of the X-ray detection bin, a CCD camera and an annular light source are arranged on the detection support, the CCD camera and the mechanical arm are both connected with a computer terminal through network cables, and a position sensor is arranged on the frame of the conveyor belt below the annular light source; the other side of the mechanical arm is provided with a qualified product outlet; a defective product box is arranged right in front of the mechanical arm, is arranged on one side of the conveyor belt and has a gap with the conveyor belt; the mechanical arm is fixedly connected with the frame of the conveyor belt; the detection bracket is fixedly connected with the conveyor belt frame; the CCD camera is arranged above the axis of the annular light source.
Preferably, the control system comprises an upper computer and a lower computer; the upper computer comprises a human-computer interaction interface, the human-computer interaction interface is arranged on a computer terminal, the computer terminal is connected with the industrial server through a network communication module, the computer terminal is connected with a mechanical motion control module through a network cable, and a casting defect detection algorithm and a product database are arranged on the computer terminal; the computer terminal is provided with an image acquisition processing module and is connected with a high-voltage cable control module through a network cable; the lower computer comprises a motion control PLC and a ray control PLC; the motion control PLC is connected with the mechanical motion control module through a network cable; and the ray control PLC is connected with the high-voltage ray control module through a network cable.
Preferably, the ray control PLC is connected with the X-ray detection cabin through a network cable; the motion control PLC is connected with the position sensor, the CCD camera, the transmission belt and the mechanical arm through network cables.
Preferably, the casting defect detection algorithm is set as a neural network model, and the neural network model forms the neural network detection model through a defect feature algorithm in the product database.
Preferably, the defective tank includes an internal defect defective tank, and a surface defect defective tank, and an internal defect defective tank are disposed at both sides of the internal defect defective tank, respectively.
Preferably, the X-ray detection bin is arranged as an n-type frame fixedly connected with the conveyor belt frame; the top surface of the n-type frame and two side surfaces of the n-type frame, which are connected with the frame of the conveyor belt, are provided with radiation-proof plates, and the other two side surfaces are provided with radiation-proof curtains which are contacted with the conveyor belt; and a radiographic inspection machine is fixed inside the X-ray detection bin along one side of the conveyor belt frame, and a radiographic detection plate is arranged on the opposite side.
Preferably, the X-ray detection cabin is connected with the computer terminal through a network cable.
Compared with the prior art, the beneficial effects of the utility model are as follows:
(1) Through the accurate combination and the cooperation of each subassembly, provide sufficient preparation for the analysis of foundry goods key surface and internal defect, can carry out accurate automated inspection and analysis, carry out classification to the foundry goods after detecting, full automation degree is high, and detection effect is good.
(2) by arranging the upper computer and the lower computer, technical support is provided for the continuity, the high efficiency and the accuracy of casting detection, and a supporting effect is provided for realizing the defects of the key surfaces of the castings and the detection of the interiors.
(3) The ray control PLC is connected with the X-ray detection cabin through a network cable; the motion control PLC is connected with the position sensor, the CCD camera, the transmission belt and the mechanical arm through network cables.
(4) The casting defect detection algorithm is set as a neural network model, and the neural network model forms a neural network detection model through a defect characteristic algorithm in a product database; the defect degree and defect type of the casting can be more accurately and rapidly processed and identified so as to carry out faster and more efficient processing and detection.
(5) The defective product box comprises an internal defect defective product box, and a surface defect defective product box, a surface defect defective product box and an internal defect defective product box are respectively arranged on two sides of the internal defect defective product box; the defective product boxes are arranged to accurately classify the types of the defective products, and independent detection is not needed during recycling.
(6) the X-ray detection bin is fixed on the conveyor belt and does not move along with the conveyor belt, so that the casting can be better detected, the radiation-proof plate can prevent radiation from escaping, and the radiation-proof curtain provides convenience for the casting to enter and exit.
(7) The X-ray detection cabin is connected with the computer terminal through a network cable; and the data of the X-ray detection bin is analyzed and stored by connecting a computer terminal.
In conclusion, the utility model is designed and utilized to realize the defect detection of important surface and inner part of the casting by the intelligent identification of the computer and the matching of X-ray flaw detection, thereby greatly improving the labor efficiency and reducing the labor cost; and casting quality detection and data management can be integrated into the same system, so that enterprises can conveniently record and manage the total quantity and the qualification rate of castings of various types.
Drawings
FIG. 1 is a schematic diagram of detection hardware of embodiment 1;
FIG. 2 is a schematic diagram of an X-ray inspection chamber;
FIG. 3 is a control system schematic;
FIG. 4 is a schematic diagram of a casting defect detection algorithm;
FIG. 5 is a schematic flow diagram of a detection system;
Fig. 6 is a detection hardware schematic diagram of embodiment 2.
In the figure, 1, a ring light source, 2, a CCD camera, 3, a detection bracket, 4, an X-ray detection cabin, 5, a mechanical arm, 6, a network cable, 7 and a computer terminal, 8 are castings to be detected, 9, a position sensor, 10, a conveyor belt, 11, a surface defect defective box, 12, an internal defect defective box, 13, a surface and internal defect defective box, 14, a qualified product outlet, 15, an X-ray detector, 16, a ray detection plate, 17 and a radiation protection plate.
Detailed Description
the present invention will be described in further detail with reference to the following specific examples, which are not intended to limit the scope of the present invention.
Example 1:
A flexible detection and analysis system for major surface and internal defects of a casting has a structure shown in figures 1, 2, 3, 4 and 5 and comprises a hardware system and a control system, wherein the hardware system comprises a conveyor belt 10 with a frame, an X-ray detection bin 4 is arranged on the frame of the conveyor belt 10, a mechanical arm 5 is arranged on one side of the X-ray detection bin 4, a detection support 3 is arranged on the other side of the X-ray detection bin 4, a CCD camera 2 and an annular light source 1 are arranged on the detection support 3, the CCD camera 2 and the arm of the mechanical arm 5 are both connected with a computer terminal 7 through a network cable 6, and a position sensor 9 is arranged on the frame of the conveyor belt 10 below the annular light source; the other side of the mechanical arm 5 (namely the tail end direction of the conveyor belt 10) is provided with a qualified product outlet 14; a defective product box is arranged right in front of the mechanical arm 5, arranged on one side of the conveyor belt 10 and spaced from the conveyor belt 10; the mechanical arm 5 is fixedly connected with the frame of the conveyor belt 10; the detection bracket 3 is fixedly connected with the frame of the conveyor belt 10; the CCD camera 2 is arranged above the axis of the annular light source 1; the defective tank includes an internal defect defective tank 12, and a surface defect defective tank 11, a surface and internal defect defective tank 13 are respectively provided at both sides of the internal defect defective tank 12.
The control system comprises an upper computer and a lower computer; the upper computer comprises a human-computer interaction interface, the human-computer interaction interface is arranged on a computer terminal 7, the computer terminal 7 is connected with the industrial server through a network communication module, the computer terminal 7 is connected with a mechanical motion control module through a network cable 6, and a casting defect detection algorithm and a product database are arranged on the computer terminal 7; the computer terminal 7 is provided with an image acquisition processing module, and the computer terminal 7 is connected with a high-voltage cable control module through a network cable 6; the lower computer comprises a motion control PLC and a ray control PLC; the motion control PLC is connected with the mechanical motion control module through a network cable; and the ray control PLC is connected with the high-voltage ray control module through a network cable.
The ray control PLC is connected with the X-ray detection cabin 4 through a network cable 6; the motion control PLC is connected with the position sensor 8, the CCD camera 2, the driving belt 10 and the mechanical arm 5 through a network cable 6.
The casting defect detection algorithm is set as a neural network model, and the neural network model forms a neural network detection model through a defect characteristic algorithm in a product database.
the X-ray detection bin 4 is an n-shaped frame fixedly connected with the frame of the conveyor belt 10; the top surface of the n-type frame and two side surfaces of the n-type frame, which are connected with the frame of the conveyor belt 10, are provided with radiation-proof plates 17, and the other two side surfaces are provided with radiation-proof curtains which are in contact with the conveyor belt 10; a radiographic inspection machine 15 is fixed inside the X-ray detection bin 4 along one side of the conveyor belt frame, and a radiographic detection plate 16 is arranged on the opposite side; the X-ray detection chamber 4 is connected with a computer terminal 7 through a network cable 6.
In the using process, when the casting 8 to be detected moves below the CCD camera 2 on the conveyor belt 10, the position sensor 9 acquires a signal of the casting 8 to be detected, so that the mechanical motion control module transmits an instruction to the motion control PLC, the conveyor belt 10 stops moving, the CCD camera 2 acquires image information of the key surface of the casting 8 to be detected, and the image information is transmitted to the computer terminal 7 through the network cable 6; after the key surface image is collected, the motion control PLC enables the transmission belt 10 to start transmission again, the casting 8 to be detected is sent to the X-ray detection bin 4, the high-voltage ray control module transmits an instruction to the ray control PLC according to the thickness of the casting, the intensity of rays emitted by the X-ray flaw detector 15 is adjusted, the ray detection plate 16 receives rays penetrating through the casting 8 to be detected, and the two-dimensional image is transmitted to the computer terminal 7; the image acquisition processing module stores images acquired by the CCD camera 2 and the ray detection plate 16, the images are processed by the casting defect detection algorithm shown in FIG. 4, the images are preprocessed by contrast enhancement, noise reduction and the like, then, image segmentation and marking are carried out on the defect target, surface defect characteristics are extracted, a neural network model is adopted in a casting defect detection algorithm, casting model parameters and defect characteristic parameters in a product database are input into the model, the neural network detection model is generated after learning and training, the casting model and the casting defect condition can be output by inputting images, finally, according to the detection result, the motion control PLC controls the mechanical arm 5 to respectively send the casting 8 to be detected to an outlet surface defect defective product box 11, an inner defect defective product box 12 and a surface and inner defect defective product box 13 according to defect classification, while the products without defects are passed through the accept outlet 14 by the conveyor 10 into the warehouse for storage.
Example 2:
a flexible detection and analysis system for major surfaces and internal defects of castings is structurally shown in figures 2, 3, 4, 5 and 6, when a casting 8 to be detected moves below a CCD (charge coupled device) camera 2 on a conveyor belt 10 in the using process, a position sensor 9 acquires signals of the casting 8 to be detected, a mechanical motion control module transmits instructions to a motion control PLC (programmable logic controller), the conveyor belt 10 stops moving, and the CCD camera 2 acquires image information of the major surfaces of the casting 8 to be detected and transmits the image information to a computer terminal 7 through a network cable 6; after the key surface image is collected, the motion control PLC enables the transmission belt 10 to start transmission again, the motion control PLC controls the mechanical arm 5 to grab the casting with the surface defect on the casting 8 to be detected into the defective product box 11 with the surface defect, the casting 8 to be detected with the surface defect is transmitted to the X-ray detection bin 4 under the movement of the transmission belt 10, the high-pressure ray control module transmits an instruction to the ray control PLC according to the thickness of the casting to adjust the ray intensity sent by the X-ray flaw detector 15, and the ray detection plate 16 receives the ray penetrating through the casting 8 to be detected and transmits the two-dimensional image to the computer terminal 7; the image acquisition processing module stores images acquired by the CCD camera 2 and the ray detection plate 16, the images are processed by the casting defect detection algorithm shown in the figure 4, the images are preprocessed through contrast enhancement, noise reduction and the like, then image segmentation and marking are performed on a defect target, the casting defect detection algorithm adopts a neural network model, casting model parameters and defect characteristic parameters in a product database are input into the model, the neural network detection model is generated after learning training, the casting model and the casting defect condition can be output by inputting the images, finally, according to the detection result, the motion control PLC controls the mechanical arm 5 to grab the casting 8 to be detected with the internal defect to the internal defect defective box 12, and the product without the defect enters a warehouse through the qualified product outlet 14 through the conveyor belt 10 to be stored.
Claims (7)
1. A flexible detection and analysis system for major surface and internal defects of a casting comprises a hardware system and a control system, and is characterized in that the hardware system comprises a conveyor belt with a frame, an X-ray detection bin is arranged on the frame of the conveyor belt, a mechanical arm is arranged on one side of the X-ray detection bin, a detection support is arranged on the other side of the X-ray detection bin, a CCD camera and an annular light source are arranged on the detection support, the CCD camera and the mechanical arm are both connected with a computer terminal through network cables, and a position sensor is arranged on the frame of the conveyor belt below the annular light source; the other side of the mechanical arm is provided with a qualified product outlet; a defective product box is arranged right in front of the mechanical arm, is arranged on one side of the conveyor belt and has a gap with the conveyor belt; the mechanical arm is fixedly connected with the frame of the conveyor belt; the detection bracket is fixedly connected with the conveyor belt frame; the CCD camera is arranged above the axis of the annular light source.
2. The system for the flexible inspection and analysis of key surfaces and internal defects of castings according to claim 1, wherein said control system comprises an upper computer and a lower computer; the upper computer comprises a human-computer interaction interface, the human-computer interaction interface is arranged on a computer terminal, the computer terminal is connected with the industrial server through a network communication module, the computer terminal is connected with a mechanical motion control module through a network cable, and a casting defect detection algorithm and a product database are arranged on the computer terminal; the computer terminal is provided with an image acquisition processing module and is connected with a high-voltage cable control module through a network cable; the lower computer comprises a motion control PLC and a ray control PLC; the motion control PLC is connected with the mechanical motion control module through a network cable; and the ray control PLC is connected with the high-voltage ray control module through a network cable.
3. The flexible detection and analysis system for the key surfaces and internal defects of castings according to claim 2, wherein the ray control PLC is connected with the X-ray detection bin through a network cable; and the motion control PLC is connected with the position sensor, the CCD camera, the transmission belt and the mechanical arm through network cables.
4. The system for flexible inspection and analysis of foundry key surfaces and internal defects according to claim 2 wherein the foundry defect inspection algorithm is configured as a neural network model that is developed by defect characterization algorithms in the product database.
5. The system for flexible inspection and analysis of key surface and internal defects in castings according to claim 1, wherein the defective cases include internal defect defective cases having surface defect defective cases, surface and internal defect defective cases disposed on both sides thereof, respectively.
6. The flexible detection and analysis system for major surface and internal defects of castings according to claim 1, wherein said X-ray inspection bin is configured as an n-frame fixedly connected to the conveyor belt rim; the top surface of the n-type frame and two side surfaces of the n-type frame, which are connected with the frame of the conveyor belt, are provided with radiation-proof plates, and the other two side surfaces are provided with radiation-proof curtains which are in contact with the conveyor belt; and a radiographic inspection machine is fixed inside the X-ray detection bin along one side of the conveyor belt frame, and a radiographic detection plate is arranged on the opposite side.
7. The flexible detection and analysis system for the key surfaces and internal defects of castings according to claim 6, wherein the X-ray detection chamber is connected with a computer terminal through a network cable.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109772724A (en) * | 2019-03-14 | 2019-05-21 | 溧阳市新力机械铸造有限公司 | A kind of flexible detection and analysis system of casting emphasis surface and internal flaw |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109772724A (en) * | 2019-03-14 | 2019-05-21 | 溧阳市新力机械铸造有限公司 | A kind of flexible detection and analysis system of casting emphasis surface and internal flaw |
| CN109772724B (en) * | 2019-03-14 | 2024-04-09 | 溧阳市新力机械铸造有限公司 | Flexible detection and analysis system for major surface and internal defects of castings |
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