CN110303056B - System for eliminating residual iron scale on surface of hot-rolled narrow strip steel based on artificial intelligence - Google Patents
System for eliminating residual iron scale on surface of hot-rolled narrow strip steel based on artificial intelligence Download PDFInfo
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- CN110303056B CN110303056B CN201811034888.5A CN201811034888A CN110303056B CN 110303056 B CN110303056 B CN 110303056B CN 201811034888 A CN201811034888 A CN 201811034888A CN 110303056 B CN110303056 B CN 110303056B
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- B21B45/00—Devices for surface or other treatment of work, specially combined with or arranged in, or specially adapted for use in connection with, metal-rolling mills
Abstract
The invention discloses an artificial intelligence-based system for eliminating residual iron oxide scale on the surface of hot-rolled narrow strip steel, which consists of a detection module and an elimination module. The detection module includes: an illumination device for providing uniform bright illumination; image acquisition means for acquiring images of high resolution and high scanning frequency; and the image processing device is used for operating a residual iron oxide scale recognition algorithm based on artificial intelligence and generating data of the iron oxide scale to be eliminated for the elimination module. The cancellation module includes: a mechanical control device for controlling the mechanical device in the elimination module; the transmission device is used for enabling the detected hot-rolled narrow strip steel to stably pass through the detection device at a high speed; and the iron scale eliminating device is used for eliminating residual iron scales on the surface of the hot-rolled narrow strip steel. The hot-rolled narrow strip production line adopting the system can fully automatically identify and eliminate the iron scale remained in the acid-free rust removal process, thereby improving the production efficiency and the product percent of pass.
Description
Technical Field
The invention relates to the field of defect detection automation, in particular to a system for eliminating residual iron scale on the surface of hot-rolled narrow strip steel by using artificial intelligence, which is suitable for a hot-rolled narrow strip steel production line.
Background
In the field of hot-rolled narrow strip steel processing, acid-free rust removal is a new environment-friendly rust removal method. However, the existing acid-free rust removal method is difficult to completely remove the iron scale on the surface of the hot-rolled narrow strip steel, and after the acid-free rust removal process is finished, the residual iron scale affecting the product quality still remains on the surface of the hot-rolled narrow strip steel. The color of the residual iron scale is darker than that of the surface of qualified hot-rolled narrow strip steel under normal conditions, and the residual iron scale can be clearly distinguished by naked eyes.
The existing acid-free rust removal production line uses naked eyes to distinguish residual iron oxide scales on the surface of the hot-rolled narrow strip steel and manually operates an iron oxide scale eliminating device to repair or eliminate the residual iron oxide scales. The iron scale eliminating process relying on the intervention of operators has low precision and low efficiency, and cannot meet the requirements of large-scale production, in particular to the production line of the hot-rolled narrow strip steel produced continuously at high speed.
Many existing methods for identifying defects in panel-shaped materials are only suitable for use with fixed-size block-shaped panel-shaped materials, such as floorboards and boards used in furniture. And such defect identification and elimination systems typically address removing the material containing the defects, not providing an option to repair the defective material; such a defect identification and elimination system cannot be applied to a hot rolled narrow strip production line for high-speed continuous production.
Disclosure of Invention
The invention provides an artificial intelligence-based system for eliminating residual iron scale on the surface of hot-rolled narrow strip steel, which aims to solve the problems of detection and elimination of the residual iron scale on the surface of the hot-rolled narrow strip steel produced continuously at high speed. The hot-rolled narrow strip production line adopting the system can fully automatically identify and eliminate the iron scale remained in the acid-free rust removal process, thereby improving the production efficiency and the product percent of pass.
The invention comprises two modules, a detection module and an elimination module:
the detection module is used for checking and identifying the residual iron scale on the surface of the hot-rolled narrow strip steel and consists of an illuminating device, an image acquisition device and an image processing device. The lighting device is used for providing uniform and bright lighting for the hot-rolled narrow strip steel to be detected; image acquisition means for acquiring a digital image of high resolution and high scanning frequency; and the image processing device is used for operating a residual iron oxide scale recognition algorithm based on artificial intelligence and generating data of the iron oxide scale to be eliminated for the elimination module.
The eliminating module is used for mechanically eliminating the residual iron scale on the surface of the hot-rolled narrow strip steel and consists of a mechanical control device, a transmission device and an iron scale eliminating device. The mechanical control device is used for controlling the mechanical device in the elimination module; the transmission device is used for enabling the detected hot-rolled narrow strip steel to stably pass through the detection device at a high speed; and the iron scale eliminating device is used for mechanically eliminating residual iron scales.
When the system operates, the hot-rolled narrow-band steel material to be detected firstly enters the detection module and then enters the elimination module. When the hot-rolled narrow-band steel material is detected, the image acquisition device acquires a digital image in the illumination range of the illumination device and transmits the digital image to the image processing device so as to perform an artificial intelligence-based residual iron oxide scale recognition algorithm on the digital image. When the residual iron scale on the surface of the hot-rolled narrow strip steel material is eliminated, the mechanical control device acquires the running speed of the hot-rolled narrow strip steel material from the transmission device and acquires data of the residual iron scale from the image processing device so as to generate an instruction for controlling the iron scale eliminating device; and the iron scale eliminating device mechanically eliminates the residual iron scale on the surface of the hot-rolled narrow strip steel according to the instruction.
The data of the iron scale may include the time when the digital image where the iron scale is located is acquired, the relative position of the iron scale in the digital image, the length and width of the iron scale in the digital image, and the category of the iron scale.
Alternatively, there may be a plurality of sets of the illumination means, the image acquisition means and the scale removing means to treat the scales on both surfaces of the hot rolled narrow strip and to enhance the scale removing effect.
Alternatively, the transmission and scale removing means may be driven by at least one of hydraulic, pneumatic, electromagnetic or servo motors.
Alternatively, the machine control can calculate the speed of the hot rolled strip through the system from PLC or encoder data.
Alternatively, the image processing device may be composed of one or more of a CPU, GPU, FPGA, PLC, or ASIC applied to artificial intelligence.
Alternatively, the artificial intelligence image recognition algorithm in the image processing device may be based on a conventional image processing algorithm, a machine learning algorithm, or a combination of the two algorithms in any ratio.
The invention has the practical benefits that the system for eliminating the residual iron scale on the surface of the hot-rolled narrow strip in the production line of the hot-rolled narrow strip produced continuously at high speed is realized, the iron scale eliminating work is fully automatic, and the production efficiency and the product percent of pass are improved.
Drawings
The features, objects and advantages of the present invention will become more apparent from the following description of non-limiting embodiments thereof, taken in conjunction with the accompanying drawings. In the drawings:
fig. 1 is a schematic perspective view of a system for eliminating residual scale on the surface of a hot-rolled narrow strip steel based on artificial intelligence according to an embodiment of the present invention.
Fig. 2 is a data link diagram of an artificial intelligence-based system for eliminating residual iron scale on the surface of a hot-rolled narrow strip steel according to an embodiment of the present invention.
Fig. 3 is a schematic image processing flow diagram of an artificial intelligence-based system for eliminating residual iron scale on the surface of a hot-rolled narrow strip steel according to an embodiment of the present invention.
Detailed Description
Hereinafter, an embodiment example of the present invention will be described with reference to the drawings of the specification. For the sake of clarity, other means not relevant to the implementation or which may be associated with a person skilled in the relevant art have been omitted from the drawings.
Hereinafter, it is to be understood that terms such as "having," "including," or the like, describe the combination of features, numbers, steps, parts, methods, acts, or algorithms disclosed in the specification, and do not preclude the possibility that one or more features, numbers, steps, parts, methods, acts, or algorithm combinations exist or are added.
FIG. 1 is a schematic perspective view of a system for eliminating residual scale on the surface of a hot-rolled narrow strip steel based on artificial intelligence according to an embodiment of the present invention. As shown in FIG. 1, the system for eliminating the residual iron scale on the surface of the hot-rolled narrow strip steel comprises a detection module S111 and an elimination module S112.
Wherein the detection module comprises devices S101-S105, as follows:
the devices S101 and S102 are the illumination means for providing uniform and bright illumination. The lighting device consists of a high-quality LED (Light-emitting Diode) array and is parallel to the hot-rolled narrow strip steel to be detected. To maintain uniform illumination, the LED array produces an illumination range that is larger than the image capture device field of view.
Alternatively, the illumination intensity may be controlled by the image processing means to adapt the image processing algorithms of the artificial intelligence to the illumination conditions in the present application example.
Alternatively, the illumination device and the image capture device may be placed in the same light-insulated container. The container of isolated light can eliminate the influence of ambient light source to the iron scale detection effect, reduces the degree of difficulty of this embodiment system debugging.
The device S103 and the device S104 are the image acquisition means for acquiring the high resolution and high scan frequency images. In this embodiment, the image acquisition device is composed of a GoPro camera, and acquires digital images with a resolution of 1280x720 at a rate of 60 frames per second. As shown in fig. 1, the image acquisition device is not parallel to the hot rolled narrow strip to be detected, so as to avoid entering the irradiation range of the reflected light and make the brightness in the acquired image uniform.
The device 105 is the image processing apparatus and is configured to run an artificial intelligence-based hot-rolled narrow strip steel surface residual iron scale recognition algorithm. In this embodiment, the image processing apparatus is a computer having a high-performance CPU. At this stage, the artificial intelligence algorithm only needs a plurality of high-performance CPU cores, but does not exclude the possibility of calling the GPU or other applicable hardware.
The data link S107 is a data link connecting the image processing apparatus and the machine control apparatus. The present specification will explain the details of the implementation of the data chain S107 in the explanation of fig. 2.
Further, the cancellation module comprises devices S106-S110, as follows:
the apparatus S106 is the actuator for passing the detected hot-rolled narrow strip smoothly through the detecting device at a high speed. This embodiment uses a motor-driven drive roller with an encoder. To reduce vibration of the material to be tested, the hot rolled narrow strip to be tested is passed through the gap between the two transfer rollers. In order to prevent the hot-rolled narrow strip steel to be detected from deforming, the length of the driving roller is larger than the width of the hot-rolled narrow strip steel and the vibration range of the hot-rolled narrow strip steel.
The equipment S109 and the equipment S110 are the scale removing device, and are used for removing residual scales on the surface of the hot-rolled narrow strip steel. The scale removing device is composed of a grinding wheel which is driven by a servo motor and can move on a guide rail. The iron scale eliminating device receives a PLC instruction generated by the mechanical control device so that the grinding wheel moves to the position of the iron scale to eliminate the residual iron scale.
Alternatively, the scale removing apparatus may use different removing methods for different types of residual scales, for example, use a smaller grinding strength for a thinner scale.
Alternatively, some of the smaller residual scale that does not affect product quality may be ignored to reduce the load on the scale removing means. The threshold value for negligible scale can be determined by both the image processing means and the mechanical control means.
The device S110 is the mechanical control means for controlling the mechanical means in the elimination module. The mechanical control device acquires the running speed of the hot-rolled narrow strip steel material from the transmission device and acquires the data of the residual iron scale from the image processing device so as to generate a PLC instruction for controlling the iron scale eliminating device.
In this embodiment, the machine control device is a PLC connected to the image processing device. The mechanical control device calculates the position to be reached by the scale removing device through the scale data acquired from the image acquiring device, and calculates and controls the time for the grinding wheel to reach the position according to the encoder data provided by the transmission device.
As shown in FIG. 1, the apparatus S101, the apparatus S103 and the apparatus S109 are combined to detect and remove residual scales on the upper surface of a hot-rolled narrow strip. Similarly, the apparatus S102, the apparatus S104 and the apparatus S110 are grouped, and the iron scale is detected and removed from the lower surface of the hot-rolled narrow strip.
Alternatively, a system for scale detection and removal of one surface of a hot rolled narrow strip consisting of the three devices can be duplicated at multiple locations to detect and remove scale from multiple surfaces and to improve the scale removal effect.
FIG. 2 is a schematic data link diagram of a system for eliminating residual iron scale on the surface of hot-rolled narrow strip steel based on artificial intelligence provided by an embodiment of the invention. As shown in the attached FIG. 2, the system for eliminating the residual iron scale on the surface of the hot-rolled narrow strip steel, which is described by the invention, comprises S211-S215 and data links among the devices, comprising S201-S204:
the devices S212-S215 are all the devices of the present invention that have been described.
The device S211 is a mechanical control device for controlling the transmission device and the scale removing device. The machine control system consists of a CPU unit of moderate computational speed. The mechanical control system is a part of a PLC control unit used in the whole production line and receives the control of a master controller of the production line and data provided by the master controller.
Alternatively, the mechanical control means may be integrated with means for running artificial intelligence image processing algorithms, both running on the same general purpose computer. Thus, the mechanical control device may use PLC or computer driven digital or analog I/O.
The data link S201 connects the actuator and the machine control device, and the actuator provides data to the machine control device for transmitting the raw data acquired by the encoder. The raw data generated by the encoder can be used to calculate the speed at which the hot rolled strip passes through the detection device. Optionally, the calculations can be combined with speed control means upstream and downstream of the production line to obtain more accurate real-time hot rolled narrow strip speed data.
The data link S202 connects the image acquisition device and the image processing device, and the image acquisition device provides data to the image processing device for transmitting a digital image with high resolution and high scanning frequency.
The data link S203 connects the image processing apparatus and the machine control system, and the image processing apparatus provides the machine control apparatus with data of the identified scales, including the time at which the digital image where the scales are located was acquired, the relative positions of the scales in the digital image, the lengths and widths of the scales in the digital image, and the categories of the scales.
Further, the data link S203 is established on the basis of the ethernet lan connection, and transmits a tcp (transmission Control protocol) packet suitable for PLC communication. The underlying protocol for network connectivity, which may be further described as Modbus TCP, is a serial network communication protocol that is commonly supported by PLC developers.
Further, the data link and any other data links need to maintain a high data transmission speed. The data transmission speed is determined according to the moving speed of the hot-rolled narrow strip steel and the scanning frequency of the image acquisition device.
The data link S204 connects the mechanical control device and the scale removing device, and the mechanical control device provides data to the scale removing device for transmitting control commands required for controlling the grinding wheel, such as the device S109 in fig. 1.
Fig. 3 is a schematic image processing flow diagram of a system for eliminating residual iron scale on the surface of a hot-rolled narrow strip steel based on artificial intelligence, which is provided by the embodiment of the invention. Fig. 3 provides only one of the artificial intelligence based image processing algorithms. The skilled artisan can generally implement the image processing objectives of the present embodiment with a variety of algorithms, including the use of other image processing codebases, or the use of machine learning related methods directly. The present embodiment runs the image processing algorithm using a high-performance computer. The algorithm used in this embodiment is composed of steps S301 to S306:
step S301 is a step of acquiring a digital image of high resolution from the image acquisition apparatus. This embodiment uses 60 frames per second digital images at a resolution of 1280x 720.
Alternatively, the image acquisition step S301 may be performed simultaneously with the following image processing steps. Simultaneous image acquisition and image processing requires a computer to have at least 2 high performance CPU cores.
Steps S302-304 are all general image processing algorithms. The implementation example adopts a corresponding OpenCV code library and carries out modification and combination required by the algorithm.
Both step S303 and step S304 use gaussian blur. Alternatively, other methods of blurring the image may be used.
Alternatively, similar calculations can be performed using other image processing codebases, such as VXL (the Vision-sensing-Libraries) or Scikis, to achieve the effects achieved by the present embodiment.
Step S302 is a step of converting the original image into a grayscale image to reduce the difficulty of image processing. In the embodiment, the hot-rolled narrow strip steel needing to be processed is gray, and the residual iron scale is dark gray or dark brown, so the image processing algorithm does not need to process the original color image.
Step S303 is a step of removing the background color of the image to reduce the influence of the uneven illumination on the image processing result. Alternatively, step S303 may be omitted if the illumination provided by the lighting device is sufficiently uniform.
Step S304 is a step of blurring the image to eliminate the influence of small scale and uneven illumination on the image processing result.
Step S305 is a step of converting the grayscale image into a black-and-white image. Alternatively, a k-means algorithm, for example, may be used to classify different colors in the image, facilitating the operation of the contour recognition algorithm.
Step S306 is a step of running a contour outline recognition algorithm. The area within the contour of the contour is darker scale.
Fig. 3 may be implemented as a computer software program according to an embodiment of the present embodiment example. The present embodiment includes a computer software program that may be stored on a computer storage medium. The computer program may invoke other hardware that may be connected to the computer, such as a GPU, FPGA, ASIC, etc. to achieve similar image processing purposes. Further, the algorithm and the data link to the scale removal means may be entirely free of a general purpose computer, the role of which may be completely replaced with an ASIC or FPGA.
The foregoing description of the invention has been presented only to illustrate the principles of the technology and apparatus employed. Those skilled in the art will appreciate that there are many different methods and systems for implementing the features described above. The scope of the invention according to the present disclosure is not limited to the technical solutions in which the above technical features and the facility system are combined, and may include other technical solutions in which the above technical features or equivalent features are combined without departing from the above inventive concept.
Claims (9)
1. The utility model provides a hot rolling ribbon steel surface residual iron scale elimination system based on artificial intelligence which characterized in that, this system comprises detection module and elimination module:
the detection module is used for checking and identifying the residual iron scale on the surface of the hot-rolled narrow strip steel, and comprises: an illumination device for providing uniform bright illumination; image acquisition means for acquiring images of high resolution and high scanning frequency; the image processing device is used for operating a residual iron oxide scale recognition algorithm based on artificial intelligence and generating data of the iron oxide scale to be eliminated for the elimination module;
the elimination module is used for mechanically eliminating residual iron oxide scales on the surface of the hot-rolled narrow strip steel and comprises: a mechanical control device for controlling the mechanical device in the elimination module; the transmission device is used for enabling the detected hot-rolled narrow strip steel to stably pass through the detection device at a high speed; the scale removing device is used for removing residual scales on the surface of the hot-rolled narrow strip steel;
when the system operates, the hot-rolled narrow-band steel material to be detected firstly enters the detection module and then enters the elimination module; when the hot-rolled narrow-band steel material is detected, the image acquisition device acquires a digital image in the illumination range of the illumination device and transmits the digital image to the image processing device so as to perform an artificial intelligence-based residual iron oxide scale recognition algorithm on the digital image; when the residual iron scale on the surface of the hot-rolled narrow strip steel material is eliminated, the mechanical control device acquires the running speed of the hot-rolled narrow strip steel material from the transmission device and acquires data of the residual iron scale from the image processing device so as to generate an instruction for controlling the iron scale eliminating device; the iron scale eliminating device mechanically eliminates the residual iron scale on the surface of the hot-rolled narrow strip steel according to the instruction;
the system is suitable for continuous hot rolling strip steel on a continuous motion production line; the residual iron scale on the surface of the hot-rolled narrow strip steel can be identified through color difference; the area of the residual iron scale on the surface of the hot-rolled narrow strip steel is not more than 50% in the obtained digital image; the residual iron oxide scale on the surface of the hot-rolled narrow strip steel can be eliminated by a mechanical tool.
2. The artificial intelligence based hot-rolled narrow strip surface residual iron oxide scale eliminating system according to claim 1, wherein the light source provided by the lighting device forms uniform illumination on the surface of the hot-rolled narrow strip to be detected, and reflected light does not enter the field of view of the image acquiring device;
the light source provided by the lighting device can pass through a scattering material or a grating type mechanism so as to improve the uniformity degree of illumination.
3. The artificial intelligence based hot rolled narrow strip surface residual scale removal system of claim 1 wherein said image acquisition device consists of a high resolution and scanning frequency camera; the image acquisition device is arranged at a position which is not in the light reflection path of the lighting device; the image acquisition device can acquire images of the hot-rolled narrow strip passing through the detection module at proper positions and angles; the image acquisition Device may be a digital camera, or a CCD (Charge-coupled Device) -based image acquisition apparatus.
4. The system according to claim 1, wherein the image processing device comprises one or more of a CPU, GPU, FPGA (Field Programmable Gate Array), plc (Programmable Logic controller), or asic (application Specific Integrated circuit) suitable for artificial intelligence;
the image processing device continuously acquires digital images of the hot-rolled narrow strip from the image acquisition device; the image processing apparatus uses conventional or machine learning based artificial intelligence image processing algorithms to identify residual scale in the digital image.
5. The artificial intelligence based hot-rolled narrow strip steel surface residual iron oxide scale eliminating system according to claim 4, wherein the image processing device provides the data of the iron oxide scale to be eliminated, including the time when the digital image where the iron oxide scale is located is acquired, the relative position of the iron oxide scale in the digital image, the length and width of the iron oxide scale in the digital image and the category of the iron oxide scale, to the mechanical control device according to the residual iron oxide scale recognition result of the artificial intelligence image processing algorithm.
6. The artificial intelligence based hot rolled narrow strip steel surface residual scale removal system according to claim 1 wherein said drive means may consist of one or more drive rolls; the transmission device can support the moving speed of the hot-rolled narrow strip steel required by the production requirement; the vibration of the transmission device does not influence the image acquisition device and the deformation of the image analysis result of the image processing device in the moving process of the hot-rolled narrow strip steel.
7. The artificial intelligence based hot rolled narrow strip surface residual scale removal system according to claim 1 wherein said mechanical control means comprises one or more of CPU, GPU, FPGA and PLC or ASIC adapted for mechanical control; the mechanical control device can generate a control instruction needing the scale removing device through the data of the residual scale and the data provided by the transmission device.
8. The artificial intelligence based hot-rolled narrow strip steel surface residual iron scale eliminating system according to claim 1, wherein the iron scale eliminating device can drive the iron scale eliminator to a required position through hydraulic, pneumatic, electromagnetic or servo motors; the scale removing device removes residual scales on the surface of the hot-rolled narrow strip steel through a mechanical tool.
9. The artificial intelligence based hot-rolled narrow strip surface residual scale elimination system according to claim 1, wherein said lighting device, said image capturing device and said scale elimination device are a set of devices capable of detecting and eliminating residual scale on only one surface of the hot-rolled narrow strip, and a plurality of sets of said devices are used to eliminate residual scale on both surfaces of the hot-rolled narrow strip and to enhance the scale elimination effect; and a plurality of groups of the same illuminating devices, the same image acquisition devices and the same iron scale removing devices are respectively arranged on the two surfaces of the hot-rolled narrow strip steel.
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| CN201436309U (en) * | 2009-06-22 | 2010-04-07 | 宝山钢铁股份有限公司 | Noncontact type electrolysis de-scaling simulation test apparatus used for strip steel sample with oxide scale |
| CN103909102A (en) * | 2012-12-31 | 2014-07-09 | 宝山钢铁股份有限公司 | Processing layout and control method for jet flow and washing of metal strip |
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