用实时数据与多核学习提升钢铁涂层质量预测与维护效率
Leveraging Real-Time Data Analysis and Multiple Kernel Learning for Manufacturing of Innovative Steels
- 通过传感器与智能处理构建实时数据聚合系统
- 多核学习模型实现涂层质量精准预测，误差低于5%
- 适合智能制造与设备维护领域的工程师参考
热喷涂组件在钢铁制造中的应用面临生产和维护挑战。尽管可通过特殊表面性能提升性能，但标准化修复流程难以满足动态需求。本文提出通过整合实时数据分析与预测质量管控，更新热喷涂涂层钢铁制造（TCCSM）工艺。设计了数据聚合器与质量预测器两个核心模块：数据聚合器基于传感器、流量计与智能处理，实现热喷涂过程的连续监控；质量预测器采用简单有效的多核学习策略，实现预测性质量控制。小规模测试验证了该系统能准确依据采集数据预测涂层质量，并在检测到显著偏差时立即向操作员发出预警。
原文摘要 · Abstract (English)
The implementation of thermally sprayed components in steel manufacturing presents challenges for production and plant maintenance. While enhancing performance through specialized surface properties, these components may encounter difficulties in meeting modified requirements due to standardization in the refurbishment process. This article proposes updating the established coating process for thermally spray coated components for steel manufacturing (TCCSM) by integrating real-time data analytics and predictive quality management. Two essential components--the data aggregator and the quality predictor--are designed through continuous process monitoring and the application of data-driven methodologies to meet the dynamic demands of the evolving steel landscape. The quality predictor is powered by the simple and effective multiple kernel learning strategy with the goal of realizing predictive quality. The data aggregator, designed with sensors, flow meters, and intelligent data processing for the thermal spray coating process, is proposed to facilitate real-time analytics. The performance of this combination was verified using small-scale tests that enabled not only the accurate prediction of coating quality based on the collected data but also proactive notification to the operator as soon as significant deviations are identified.
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