【摘要】 板带冷轧是钢铁制造流程的关键环节，但长期以来面临着板形缺陷、厚度波动和轧机振动等问题，这些因素显著影响冷轧生产效率和产品质量。机器学习技术通过分析海量工艺数据，为实时预测和消除潜在缺陷提供解决方案。基于历史与实时数据，机器学习算法能够识别轧制力、辊缝、轧速等工艺参数与板形、厚度均匀性等质量指标间的复杂关联规律，实现工艺参数的动态优化，在保证产品一致性的同时有效降低废品率和停机时间。机器学习驱动的预测模型支持对轧制过程进行前瞻性调控，从源头上减少缺陷产生，提升整体效能。机器学习技术的应用不仅提高了冷轧过程的精度与可靠性，更带来显著的成本节约和产能提升。更多还原

【Abstract】 The strip cold rolling process is a critical component of iron and steel manufacturing, yet it is often plagued by challenges such as flatness defects, thickness variations, and rolling mill vibrations,which can significantly impact productivity and product quality. Machine learning(ML) has emerged as a powerful tool to address these issues by analyzing vast amounts of process data to predict and mitigate potential defects in real-time. By leveraging historical and real-time data, ML algorithms can identify complex patterns and correlations between operational parameters(such as roll force, roll gap, and rolling speed)and key quality indicators like flatness and thickness uniformity. This enables the optimization of process parameters dynamically, ensuring consistent product quality while minimizing waste and downtime. Furthermore, ML-driven predictive models facilitate proactive adjustments to the rolling process, reducing the occurrence of defects and enhancing the overall efficiency. The integration of ML not only improves the precision and reliability of the cold rolling process but also leads to substantial cost savings and increased productivity.更多还原