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Overview Mold is the casting billet forming equipment in continuous steel casting, and it is also the core equipment and key technology of continuous casting machine. Its function is to forcibly cool the high-temperature molten steel continuously injected into its inner cavity through the water-cooled copper plate to export its heat and to gradually solidify it into a casting billet with the required cross-sectional shape and billet shell thickness. And the casting billet, which is still in liquid state, is continuously pulled out from the lower nozzle of the mold to create conditions for casting billet to be completely solidified in the subsequent secondary cooling area. The main function of the mould oscillation device is to make the mould perform mechanical movement according to the given characteristics of amplitude, frequency and waveform deflection. The purpose is to facilitate demoulding and prevent the occurrence of sticking and breakout accidents due to the bonding of the casting billet with the copper wall of the mold during the solidification process. MOC-K100 mould oscillation online monitoring system can three-dimensionally detect the positive and negative frequencies and waveform characteristics of the mould oscillation. The online changing data is provided in real time, which provides convenient and effective detection methods for on-site inspection personnel. The meaning of the system 1. When the continuous casting machine is not working properly, the billet quality problems (such as unstable oscillation marks, edge cracks, etc.) occur. According to the monitoring results of the system, we can combine with the actual on- site situation to analyze the cause of the failure, and efficiently evaluate and solve the problem of the oscillation table to improve product quality. 2. During the normal operation of the equipment, with the fatigue and wear of the mechanical structure of the mold, the expected oscillation parameters of the mold and the actual oscillation parameters will have a oscillation difference with time change, resulting in the internal process parameters’ changing of the continuous casting machine. Timely and accurate monitoring of changes in these parameters is helpful to schedule maintenance. 3. Transfer equipment maintenance from regular maintenance to state maintenance, do not repair when there is no problem, and arrange maintenance according to production needs when abnormal signs appear. Prevent over-repair and under-repair. 4. According to the monitoring results of the equipment, the on-site working parameters are adjusted in time to avoid adopting inaccurate or large-error oscillation to work and prevent the "breakout" accident. 5. We can timely find out that the oscillation is not stable or out of control in a small range, so as to avoid the formation of unevenness and unstable oscillation marks on the surface of the slab during the solidification of molten steel into the slab, and improve the surface quality of the product. 6. Adjust the working parameters reasonably according to the monitoring results, avoiding the use of excessive safety factor, making the entire assembly line play a normal working efficiency, increasing the output of billets, and create favorable conditions for increasing the casting speed. The functions of the system (1) Overview of monitoring parameters: Provides an overview of real-time monitoring parameters in the full screen, displaying all real-time data including oscillation parameters and process parameters. (2) Time domain waveform: This function mainly displays the oscillation waveforms of multiple measuring points and one channel standard waveform in real time, and simultaneously displays the three elements of oscillation of each measuring point in real time. (3) Waveform superposition: The difference between this function and the time domain waveform is that the waveforms of multiple measuring points are superimposed on a graph with lines of different colors on the same scale, and the oscillation difference of each channel can be calculated and displayed intuitively. (4) FFT: This functional module completes the calculation and display of the oscillation spectrum in real time. It is used to evaluate the waveform distortion of the entire oscillation table and to determine whether there is a problem with the installation of the oscillation device. (5) Bar graph: The oscillation of each measuring point is indicated by a bar graph of peak value and sensor gap voltage, which is more intuitive. (6) Trend: After starting this module, the operator can search the historical records in the database, analyze the changing trend of each parameter, and use it to analyze the rationality of the steel quality and processing parameter settings for different steels under the oscillation conditions. The parameters of the system 1. Number of measurement channels: According to the mold design, up to 16 sensors can be arranged 2. Sensor: three-dimensional oscillation sensing (X, Y, Z three axes) 3. Sensitivity: 1000mV/g 4. Frequency range: 0.04~10HZ 5. Frequency accuracy: ±0.01 Hz 6. Amplitude accuracy: 0.005mm 7. Transmission mode: wired digital transmission, wireless transmission 8. Host: 3U standard rack, handheld device 9. Sensor power supply: 24V.DC 10. Host power supply: 220V.AC/50Hz Mold oscillation curve The structure of the system The main components of the continuous casting mould online oscillation detection system are: 1. Three-dimensional oscillation measurement sensor 2. Handheld oscillation sensor calibrator 3. Dedicated signal connection cable 4. Preamplifier 5. Operation processing unit 6. Graphic display terminal 7. Industrial Ethernet (TCP/IP) or PROFIBUS-DP communication components 8. Control cabinet

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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.

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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.