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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.

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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.

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Overview In order to ensure the quality of the slab, it is necessary to strictly control the performance and various parameters of the CCM’s guide roll within a certain error range. Due to the continuous casting machine (CCM) is large and in a harsh production environment, it is difficult to measure the performance of the continuous casting machine manually, and many parameters are difficult to measure. Therefore, it is very important to have a set of equipment that can simulate drawing of steel billet to realize the measurement of CCM’s various parameters and performance. The multifunctional strand condition monitor uses various sensors to realize the measurement of CCM’s roll gap value, the arc value of continuous casting section, the rotation quality of the rolls, and the water spray pressure under the condition of simulating drawing of steel billet. Therefore, the CCM’s quality can be controlled in real time, and it is convenient for the maintenance personnel to analyze the CCM’s measurement results. The problem areas of the casting machine equipment can also be found. If these problems doesn’t be found in time, it will cause the bad quality of CCM’s products, the steel breakout in pouring process, surface and internal cracks and centerline deviation. The measurement of the strand condition monitor Roll gap value： By measuring the distance between a series of inner arc guide rolls and outer arc guide rolls on the CCM’s roller bed. It can be used to set the correct roll gap spacing for each pair of guide rolls in the slab caster. Arc condition of guide roller ： Measuring the position between the position of the CCM’s outer arc guide roll and the two adjacent guide rolls and then measuring the deviation compared with the origical position of CCM’s guide roll, we can obtain the arc condition of the outer arc guided roller. Guided roller rotation： It is possible to know whether each guided roller can rotate freely by measuring. Water spray pressure： The operation of the water spray cooling system can be judged by measuring the situation of secondary cooling water spray at different positions of CCM's width direction. Then we can accurately locate whether the water spray at a certain point is normal. The main components of the strand condition monitor 1. The main body of the strand condition monitor used to measure the continuous casting sector 2. The connecting chain between strand condition monitor and dummy bar 3. The storage rack chain of strand condition monitor 4. Battery charger: 24V 5A 5. Calibration equipment 6. Remote control 7. Laptop 8. Printer 9. Data communication cable The technical parameter 1. The measurement range of roll gap: 210mm, 230mm (can be customized according to requirements) 2. The measurement accuracy of roll gap: 0.01mm 3. The arc measurement range: ±80° 4. The arc measurement accuracy: 0.01° 5. The spray measurement range: 0-10 6. The spray measurement accuracy: ±1 7. The battery usage times: 3 times

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