1, automatic print quality inspection
The detection system used in the automatic print quality inspection equipment is to first take a standard image using a high-definition, high-speed camera lens, and set a certain standard on this basis; then, the image to be detected is taken and the two are compared. The CCD linear sensor converts the change in the light quantity of each pixel into an electronic signal. After the comparison, if the detected image is different from the standard image, the system considers the detected image to be a defective product. The various errors generated in the printing process are only different from the standard image and the detected image for the computer, such as defects such as smudges, ink spots, and the like.
The earliest used for print quality inspection is the technology of comparing the gray scale of the standard image with the detected image. Now the more advanced technology is based on the RGB three primary colors for comparison. Where is the difference between fully automated machine testing and human eye detection? When human eyes are regarded as examples, when we concentrate on a printed matter, if the contrast of the printed matter is strong, the smallest flaw that the human eye can find, Contrast color is not less than 0.3mm defects; but it is difficult to maintain a consistent and stable visual effects depending on human capabilities. In another case, if defects are found in prints of the same color, especially in a light color system, the defects that the human eye can detect need at least 20 gray levels. An automated machine can easily detect a 0.10mm defect, even if the defect is only one grayscale difference from the standard image.
However, in terms of practical use, even the same panchromatic contrast system has different ability to discriminate color differences. Some systems can find contours and defects with large variations in color, while others can identify very small defects. For white cardboard and some simple style prints, such as the Japanese KENT cigarette label, the United States Marlboro cigarette label, simple detection may be sufficient, and most of the domestic print, especially various labels, has many characteristics, With too many flash elements, such as gold, silver cardboard, hot stamping, embossing, or polishing prints, this requires that the quality inspection equipment must have sufficient ability to find the smallest grayscale differences, perhaps 5 shades of gray. The difference in level may be a stricter one gray level difference. This point is crucial to the domestic label market.
The accuracy of the comparison between the standard image and the printed matter being examined is a key issue in the detection equipment. Usually, the detection equipment collects the image through the lens. In the middle part of the scope, the image is very clear, but the image at the edge may be false. Shadow, and the detection result of the phantom part directly affects the accuracy of the entire test. From this point of view, if only the comparison of the full width area is not suitable for some fine prints. If the obtained image can be subdivided again, for example, the image is divided into 1024dpi X 4096dpi or 2048dpi X 4096dpi, the detection accuracy will be greatly improved, and at the same time the false detection of the edge portion is avoided, so that the detection result is more stable.
The use of inspection equipment for quality inspection provides real-time reporting of the entire process and detailed and complete analysis reports. The on-site operator can rely on the timely detection of the automatic detection equipment and timely adjust the problems in the work according to the real-time analysis report. It is possible that the reduction will be not only a percentage point scrap rate, the manager can analyze the report based on the test results. Tracking the production process is more conducive to the management of production technology. Because of the high-quality testing equipment required by customers, not only does it stop at the quality of the printed matter, but it also requires the ability to analyze afterwards. Some quality inspection equipment can not only improve the qualification rate of finished products, but also help manufacturers to improve the process and establish a quality management system to achieve a long-term stable quality standards.
2, gravure printing machine position control and product testing
The video image of the printed product is continuously taken by the camera set on the production line, and the speed of the recording is 30 frames/s or less and adjustable. The image captured by the camera is first quantified, the analog signal is converted into a digital signal, and a key frame that effectively represents the content of the lens is extracted from it and displayed on the display. For a frame of image, an analysis method for a still image may be used to process. Through size measurement and multi-spectral analysis, color patches on a video image may be identified, and color space and color parameters of the color patch may be obtained as well as some other correlations. Due to various factors, various types of noise may occur, such as Gaussian noise, salt and pepper noise, and random noise.
Noise brings many difficulties to image processing. It has a direct impact on image segmentation, feature extraction, and image recognition. Therefore, real-time acquired images need to be filtered. Image filtering requires the removal of noise outside the image while maintaining the details of the image. When the noise is Gaussian noise, the most commonly used linear filter is easy to analyze and implement; however, the linear filter has poor filtering effect on salt and pepper noise, and the traditional median filter can reduce the salt and pepper noise in the image, but the effect Not ideal, that is, fully dispersed noise is removed, and the noise close to each other will be retained, so when the salt and pepper noise is more serious, its filtering effect is significantly worse. This system improved median filtering method. The method firstly obtains the median value after removing the maximum and minimum grayscale pixels in the noise image window, and then calculates the difference between the median value and the corresponding pixel grayscale value, and then compares it with the threshold to determine whether to use the obtained value. Instead of the grayscale value of the pixel.
In this stage, image segmentation detects each color standard and separates it from the background. The edge of the object is represented by gray-level discontinuity. The L-edge type can be divided into two types. The first is the step edge, and the pixels on both sides are The gray value is significantly different; the second is the roof-like edge, which lies in the change of the gray value from increasing to decreasing turning point L. For the step edge, the second-order directional derivative is zero-crossing at the edge, so the differential calculation is available. Son to do edge detection operator. The differential operator class edge detection method is similar to the high-pass domain high-pass filter and has the effect of increasing high-frequency components. Such operators are quite sensitive to noise. For the step edge, the commonly available operator is the gradient operator Sobel operator. Child and Kirsh operators. Laplace transforms and Kirsh operators are available for roof-like edges. Because the color mark is rectangular and the gray levels of adjacent edges are quite different, edge detection is used to divide the image. Here we use Sobert edge sub-edges for edge detection. It uses local differential operators to find edges and can better separate the color marks. In the actual detection process, a color image edge detection method is adopted, and an appropriate color base (such as intensity, chroma, saturation, etc.) is selected for detection. According to the type characteristics of the printing press, that is, the color of the printing machine and the characteristics of the layout, multi-threshold processing is performed to obtain binary maps of various colors.
The segmented image is measured and the object is identified by the measured value. Since the color patch is a rectangle with a regular shape, the following features can be extracted: (1) Calculate the area of a rectangle by pixels, (2) Rectangularity, (3) ) Chromaticity (H) and Saturation (S ), and then the spacing between the color swatches is obtained according to the number of pixels in the interval between the color swatches. Compared with the set value, the difference between the two is obtained, and a total of m measurements are taken and the average is taken. The difference provides a corresponding adjustment signal to the digital AC servo adjustment section. To adjust the relative position of the color roller to eliminate or reduce misalignment. In feature extraction, multi-spectral image analysis is performed on the image to quantitatively represent color patches, such as the color of pixels in a color number image. The HIS format is used to obtain two parameters of the color information of each color standard: chroma and saturation. To check the quality of the ink. The statistical analysis of the binary image of each color and the matching of the template with the standard graphics are performed to measure the parameters such as the ink scraps during the printing process.
The printing machine is unwound from the uncoiler and passes through the printing units in sequence to carry out printing and drying of various colors. The winding machine carries out the winding and each color printing will print the color mark on the edge of the printing material for color registration. The color markings are 10 mm horizontal and 1 mm wide. The marking lines of each adjacent color should be parallel to each other when they are accurately printed, and the vertical (vertical) distance is 20 mm. Video images of printed products are continuously taken by the cameras installed on the production line. , Through the size measurement and multi-spectral analysis can identify the color image on the video standard, to get the color code spacing and color parameters of the color L If the adjacent two color color code interval is greater than or less than 20 mm, it indicates overprinting deviation. The deviation signal is sent to the servo variable frequency drive unit to drive the AC servo motor to move the corresponding chroma correction roller ML up and down to extend or shorten the dynamic correction of the printing material from the previous unit printing plate roller to the printing plate roller.
3, application in the modern packaging industry
In the automated production of modern packaging industry, it involves a variety of inspections and measurements, such as the printing quality inspection of beverage bottle caps, bar codes and character recognition on product packaging, etc. The common feature of this type of application is the continuous mass production and the very high demands on the appearance quality. Normally this kind of highly repetitive and intelligent work can only be accomplished by manual inspection. We often see hundreds or even thousands of inspection workers behind the modernization lines of some factories to perform this process. While the factory has increased tremendous labor costs and management costs, it still cannot guarantee 100 inspection qualification rates (ie, zero defects), and today's companies have not allowed even 0.1 defects to exist. In some cases, such as precise and rapid measurement of a small size, shape matching, color identification, etc., it is impossible for the human eye to perform continuously and stably, and other physical quantity sensors are also difficult to use. At this time, people began to consider the rapidity, reliability, and reproducibility of the results of the computer, thereby introducing robot vision technology.
In general terms, first the CCD camera is used to convert the captured target into an image signal, which is then sent to a dedicated image processing system and converted into a digital signal based on pixel distribution, brightness, and color information. The image system performs various operations on these signals. Extract the characteristics of the target, such as: area, length, quantity, position, etc. Finally, output the result according to the preset tolerance and other conditions, such as: size, angle, offset, number, pass/fail, yes/ Nothing. Machine vision is characterized by automation, objectivity, non-contact, and high precision. Compared with image processing systems in the general sense, machine vision emphasizes accuracy and speed, and reliability in industrial field environments.
Machine vision is extremely suitable for measurement, inspection, and identification in mass production processes, such as the identification of printed characters on the surface of ICs, the identification of production dates on food packaging, and the inspection of label placement locations.
A typical vision system generally includes the following parts: a light source, a lens, a CCD camera, an image processing unit (or image capture card), an image processing software, a monitor, a communication/input/output unit, and the like. The output of the vision system is not the image video signal, but the detection result after the operation processing, such as the size data. After the host computer such as PC and PLC obtains the test result in real time, the command motion system or I/O system performs corresponding control actions such as positioning and sorting. From the visual system operating environment classification, can be divided into PC-baseD system and PLC-baseD system. PC-based systems take advantage of its openness, high programming flexibility and a good Windows interface, while the overall system cost is lower. US DATAT
Silakan tinggalkan kami pesan
Privacy statement: Your privacy is very important to Us. Our company promises not to disclose your personal information to any external company with out your explicit permission.