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The composition of the system The photoelectric strip automatic center position control system is mainly composed of sensors, amplifiers, PLC controllers, hydraulic control and other parts. System composition block diagram The main technical parameters 1. Detection width: 300-2800mm 2. Detection light source: 220VAC. (100VAC) 30W. High frequency white light works continuously 3. Photodetector: precision die-casting components, time constant 20ms 4. Maximum working voltage: 50VDC 5. Distance from receiver to emitter: 300-1000mm 6. Operating temperature: 0℃-60℃ 7. Resolution: 0.5mm 8. Linearity: ±1%F.S (after linearization) 9. Repeatability error: △R1%, F.S 10. Servo-valve working signal: ±10V 11. Rated pressure (oil pressure): 4.5Mpa 12. Rated flow: 30L/min 13. Symmetry: 10%

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Overview: The BPS-K600 continuous casting mould breakout prediction system is developed by Wuhan CenterRise M&C Measuring Co., Ltd. It adopts artificial neural network and combines with traditional artificial intelligence and information processing technology to overcome the defects of traditional logic-based prediction models. It has the functions of self-adaptation, self-organization and real-time learning. The breakout prediction system has an optimized industrial network structure. According to the distribution and change rules of the temperature field of the mold, it can track and respond to the breakout process in real time. It can learn and judge by itself, and provide early warning information for on-site operations. It can effectively prevent the occurrence of breakout accidents, greatly reducing the cost and improving the safety factor of continuous casting. The system is suitable for all kinds of continuous casting machines such as square billet, round billet, rectangular billet and slab. Block Diagram of Breakout Prediction System The composition of the system Thermocouple: according to the on-site conditions of the mold, water tank, bolts and mold frame to desgin Front-end acquisition system: Multi-module acquisition chain, industrial Ethernet interface, high-speed embedded processing module Working platform: Real-time display of mold temperature field status, online analysis and prediction of mold breakout Server: Data exchange and storage management, reporting and printing system, with data analysis and self-learning functions The Main technical parameters Data sampling: the sampling frequency of K-type/T-type thermocouple can up to 10HZ Accuracy of temperature measurement: ±0.25℃ Support communication protocol: Industrial Ethernet (TCP/IP)，PROFIBUS-DP，CANopen Executive standard: DE0411 Standard Class III/1EC584/IEC1515 Transmission speed: 100M/bps The composition of the software Online monitoring software for breakout prediction Offline query software for breakout prediction Data recovery and self-learning software packages Data acquisition and monitoring software The software package of mold thermal status display The functions of the system Real-time data acquisition, data analysis and judgment Automatic selection of steel grade’s alarm parameters Real-time dynamic picture monitoring Prediction alarm and information display of breakout signs Automatic deceleration after alarm Historical data storage and historical trend graph display Automatic report generation and printing Alarm prediction and pattern recognition of unknown steel grades by neural network Dynamic real-time temperature profile prediction Mould heat flow field display FIG 1：Typical process of mold breakout prediction The features of the system 1. Reasonable design of temperature measurement point and professional protection front-end acquisition system, and complete installation and calibration tool components. 2. The system has strong adaptability and can meet the transformation of various structural continuous casting machines. 3. High-speed data communication, using industrial network to form an independent local area network for each workstation and server. It can realize high-speed data transmission of alarm files, alarm parameters, and other databases. 4. The combination of artificial neural network and intelligent forecasting model improves the alarm accuracy of breakout forecasting. 5. Historical data storage and historical trend graph display, historical reports, historical alarm legend printing, real-time alarm data printing. 6. Provide a local area network interface, which can realize the storage and forwarding of alarm files, and the input and output of offline alarm data. 7. Remote Web browsing can be achieved according to customer needs. 8. Can be seamlessly connected with MES-C220 mould expert system.

Overview： Mold powder is mainly used in the continuous casting and steelmaking process, evenly sprinkled on the molten steel surface in the mold to lubricate, keep warm, prevent oxidation and absorb non-metallic inclusions. Mold powder is an indispensable auxiliary production material in the production process. In the process of evenly distributing the mold powder to the molten steel surface of the mold, the amount of mold powder should be controlled, and the thickness of the mold powder should be within a certain range. If the coverage thickness of the mold flux is too thin, it will cause no heat preservation, oxidation of molten steel, inclusions, cracks, large drawing resistance, steel leakage, etc. At the same time, the coverage thickness of mold flux should not be too thick, they can’t melt in time with too much mold powder, if the molten steel is churning, it is easy to get the mold powder into molten steel at this time, resulting in mold leakage. Therefore, the amount of mold powder must be controlled within a reasonable range. The detection and control of mold flux thickness is very important, especially for the steel plants which are strict with the steelmaking production process. The detection principle： The mold flux detection system developed by Wuhan CenterRise M&C Engineering CO., Ltd. uses the laser triangular sensor fixedly installed above the mold to obtain the height h1 by scanning the upper surface layer of mold flux with high-precision laser triangulation. When the laser measuring head is placed each time, the height h3 of the upper nozzle of the mold will be marked, and the system through the PLC system which is connected to the continuous casting machine through the network to introduce the height h1 of the mold liquid level. Through the calculation of three heights, we can get the thickness of mold powder H = 900-h1-(h3-h2)mm (900mm is the depth of mold). The features： The system adopts a high-precision laser triangulation distance meter, which can measure the thickness of the mold flux with high speed and high precision, and the accuracy can reach 1mm. The equipment is easy to install and use. When we are using, the laser rangefinding sensor can be placed directly above the mold without affecting production. The system can be interlocked with the automatic mold powder feeding system to automatically adjust the amount of mold powder, so as to automatically control the amount of mold powder within a reasonable range and realize intelligent automatic mold powder feeding. Non-contact measurement, the system only needs ventilation protection, simple and stable to use. The parameters： Measurement range：0-50mm Temperature measurement range: -20℃-700℃ Equipment installation distance：750mm System measurement accuracy：1mm Laser measurement accuracy: 0.1mm Frequency: 700-2000Hz Cooling method: air-cooled purging Installation method: rail sliding installation, can be slid out during maintenance Storage period: data can be stored for more than 2 years Communication method: Industrial Ethernet or Profibus-DP

Overview The surface quality inspection of high temperature cast product is of great significance to the repair of cast product’s defects and the hot delivery of hot cast product. The ZSQ-T series of online surface quality inspection equipment for casting billets developed by Wuhan CenterRise M&C Engineering Co., Ltd can detect the defects such as scratches, cracks, holes, pits, edge cracks, edge defects on the surface of the cast products, and meet the customer's requirements for the quality inspection of the surface of the cast product. The principle of the system The ZSQ-T200 online surface quality inspection system for continuously cast products adopts the principle of optical imaging and uses a CCD image sensor to input the surface image of the cast products to be detected into the computer, and then the obtained image is processed by special image processing software to find out whether there is any defect in the image. After the defects in the image are obtained, the obtained defects are classified through a special self-learning system, and finally the requirements of the computer to automatically identify the surface defects of the cast products are achieved. The composition of the system 1. Optical part: The optical part is composed of multiple fixed-focus lenses and linear CCD, which mainly realizes scanning and imaging of the surface of the cast products. 2. Lighting part: The lighting part is composed of a high-brightness LED array and a cylindrical lens, which realizes high-brightness and high-uniformity lighting on the surface of the cast products. 3. Mechanical structure part: The mechanical structure part mainly realizes the adjustment, installation and fixing of the optical part and the lighting part, and consists of the imaging head (optical part), light box (lighting part), bracket and control cabinet. The structure is required to be stable and reliable. 4. Real-time processing: The real-time processing part is completed by the multi-CCD data real-time sampling processing unit (high-speed DSP) independently developed by Wuhan CenterRise M&C Engineering Co., Ltd. 5. Hardware part: The hardware part consists of high-speed storage controller and storage hard disk array, Gigabit InternetMAC controller, Gigabit Ethernet PHY. Rj45, and multiple high-speed workstations. 6. Detection software: The detection software mainly realizes the functions of collection, storage, transmission, processing of CCD scanning images, extraction of defects, analysis of defect characteristics, calculation of defect size and position, storage of defect images, generation of cast product’s surface quality analysis reports. etc.

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