The 'full process quality management system' with the concept of quality priority is one of the application models of the development direction of intelligent manufacturing and an important part of the overall quality information construction of steel plants. In system design, the overall structure should be planned and implemented in steps from the perspective of the overall quality management of the steel plant, and seamlessly integrates the production process quality information of the overall information system, MES and first- and second-level systems based on the factory area. Realize the functions of collecting process quality data, online quality monitoring, quality prediction, online quality judgment, full process quality traceability, quality improvement management and other functions in the entire process from steelmaking to finished products.
Process Quality System (SPC): Collect process information data for steelmaking, hot rolling, cold rolling, thick plates, rod wires and other processes, provide user analysis and statistics (pass rate, trend table, process capability), and provide continuous process data graphical curve comparison
Automatic process quality determination system: automatic determination function of hot rolling mass (thickness, width, finish rolling temperature and coil temperature), cold rolling mass (thickness, width, temperature)
Steel embryo quality evaluation system (steel embryo QE): Analyze based on historical quality control data, and use the various process conditions of steelmaking and the numerical control to construct the quality evaluation logic, collect various process signals of steelmaking production equipment, conduct steel embryo quality evaluation, and provide steel embryo suggestions and block information; further summarize the disposal experience to achieve the effect of abnormal disposal suggestions
Full process process quality system: build an integrated platform that connects upstream and downstream process process information, conducts cross-process quality data integration and analysis, and thus provides effective quality problem analysis and management
Machine prediction system: collect process parameters and quality data of each process, use big data analysis technology to mine the relationship between process and quality, achieve online machine quality prediction and early warning, avoid the flow of semi-finished products with inconsistent quality or abnormality to the sub-process, and ensure the stability of product performance
Integration of quality management and QMS: Relying on the big data of production process performance constructed by QMS, quality personnel can use QMS's big data analysis tools to optimize process parameters, predict and assist in metallurgy standard design to form a complete system of quality management dead cycle.
Cost reduction: reduce the number of steel embryo inspections, protect equipment and personnel, summarize and summarize disposal experiences and reduce costs
Early warning: Discover and resolve problems early, prevent the expansion of the risk of quality non-compliance, and ensure that steel product availability and improvement are met
Disposal: For steel embryos that have been detected abnormal, suggest re-test or re-treatment remediation measures to reduce the inventory and capital backlog of overflowed steel embryos
Improvement: Accumulate past production performance and quality assessment information as a driving force for continuous quality improvement
Stability: To improve the stability of the process and reduce downgrades or retractions caused by process changes
Integration: The entire process quality analysis system widely contains data from all ends, allowing users to only check on a single platform to improve work efficiency
