On May 19, the Ningbo Steel Digital Product Quality Management System Research Project (Ningbo Steel QMS System), contracted by IET,USTB successfully passed acceptance inspection. Organized by the Manufacturing Management Department of Ningbo Steel, the acceptance panel included representatives from the Technical Center, Operation & Reform Department, end-user departments and the IET,USTB project team. The fully integrated intelligent quality control platform covering steelmaking and 1780mm hot rolling lines marks another major breakthrough for IET,USTB in metallurgical digital quality management, offering a typical demonstration for full-process digital, intelligent and lean transformation of steel enterprises.

Targeting quality control pain points across steelmaking and 1780mm hot rolling processes, the QMS system centers on data connectivity, intelligent judgment, full-chain traceability and in-depth analysis. It builds an integrated intelligent control platform covering six core modules: data collection, process standard management, real-time quality judgment, full-lifecycle traceability, quality data analysis and cross-departmental quality collaboration. The system unifies quality information flows across all working procedures from raw materials, steelmaking, refining and continuous casting to hot rolling, realizing closed-loop quality control and shifting quality management to a "prevention-first, real-time monitoring, full traceability" model. During project construction, the IET,USTB team worked on-site to overcome technical hurdles and built a system with five core capabilities:

01 Full-Coverage Omni-Domain Data Collection

The system fully accesses and synchronizes quality data from L1/L2 control systems, ERP platforms, inspection labs and large-scale instruments covering the entire production chain from molten iron to hot rolled coils. Adopting differentiated collection strategies to capture millisecond-level data, the platform standardizes and integrates multi-source data via dual links of Kafka and APIs, effectively eliminating data silos and providing high-real-time, multi-dimensional data support for quality judgment, traceability and analysis.

02Precise and Efficient Intelligent Quality Judgment

The system constructs a multi-dimensional rolling process evaluation system, breaking down core quality indicators including thickness, width, finishing delivery temperature, coiling temperature, crown and wedge into refined judgment parameters for head, tail and continuous out-of-tolerance sections. Combined with visualization technology, it enables precise localization of quality defects with a 100% accuracy rate for key indicator judgment. Meanwhile, customizable tolerance bands and flexible configurable judgment rules support complex control scenarios for multi-variety and multi-specification products, realizing refined, visualized and dynamically adjustable real-time process judgment.

03 Visualized and Controllable Full-Chain Traceability

A multi-dimensional traceability system based on time axes, length axes, material numbers and furnace batches is built, supporting flexible queries by working procedure, equipment, order and steel grade, with exclusive traceability functions for hot rolling surface inspection instruments to meet all types of full-production-line data traceability demands. The system supports millisecond-level real-time and historical curve tracing, comparative analysis of curves for multiple materials, and one-click integration of full-process data, greatly improving efficiency for quality problem source tracing and troubleshooting.

04 Data-Driven In-Depth Quality Analysis

Integrating data algorithms with metallurgical processes, the platform develops quantitative analysis models for seven major defect themes including longitudinal cracks, inclusions, flatness defects and peeling, enabling accurate identification of parameter abnormalities and calculation of process risk coefficients to replace traditional experience-based judgment with data-driven decision-making. It also launches analytical modules for correlation analysis, SPC and clustering, automatically calculating CP/CPK indices and generating multiple control charts to realize pre-judgment and stable control of process quality, boosting the scientificity of process decision-making.

05 Efficient Collaborative Quality Management

Multi-level KPI dashboards for daily and monthly quality reports covering steelmaking and rolling procedures are deployed, catering to management demands at factory, procedure and team levels. The dashboards support drill-through linkage between overview indicators and detailed data, enabling quick positioning of specific steel coils, defective slabs and root causes from general summaries. Data barriers between steelmaking and hot rolling are eliminated to support cross-procedural collaborative disposal, significantly improving the efficiency of full-chain coordinated quality management.

The successful acceptance of the Ningbo Steel QMS project represents a key achievement of integrated industry-university-research-application practice by IET,USTB and a critical step for Ningbo Steel to build a benchmark future factory in Zhejiang Province. It will further consolidate Ningbo Steel’s foundation of digital intelligent control, stabilize product quality and enhance core competitiveness. Moving forward, IET,USTB will continue to leverage its technical strengths in metallurgical intelligent manufacturing, deepen cooperation with outstanding industry partners including Ningbo Steel, and focus on AI quality large models and intelligent production line upgrading. It will continuously inject momentum into digital transformation of metallurgical enterprises and development of new productive forces, jointly shaping a new intelligent future for the steel industry.