Transverse thickness difference is a key indicator for evaluating the quality of cold-rolled silicon steel products,jointly determined by hot-rolled silicon steel data and cold rolling process parameters.However,there...Transverse thickness difference is a key indicator for evaluating the quality of cold-rolled silicon steel products,jointly determined by hot-rolled silicon steel data and cold rolling process parameters.However,there are“process barriers”and“data islands”between different production lines,resulting in low accuracy and poor interpretability.In addition,the transverse thickness difference mainly relies on manual sampling measurement.The lag and uncertainty of the measurement results lead to a lack of effective online control methods.To overcome this,a cold-rolled silicon steel transverse thickness difference control framework based on interpretable machine learning is proposed.First,a hot-cold rolling cross-process data platform is established to match and integrate multivariate data from different production lines,providing a data foundation.Then,an RUN-HLSSVM-AdaBoost model prediction model combining Runge-Kutta algorithm optimized hybrid kernel least squares support vector machine and AdaBoost ensemble modeling method is established.Afterward,the adaptive bandwidth kernel density estimation improved by local weighting strategy is used to construct a prediction interval,which characterizes the uncertainty of the prediction results.SHAPley Additive exPlanations interpretable method is used to break the“black box”limitation and reveal the influence of hot and cold rolling parameters on the transverse thickness difference,and finally,an online control strategy is proposed.Industrial experiments have verified the effectiveness of the above framework,and the transverse thickness difference has been significantly improved,which provides a new paradigm for solving the problem of online control of transverse thickness difference of cold-rolled silicon steel.展开更多
冷轧带钢板形控制技术是钢铁工业领域的核心控制技术之一,其控制效果直接决定了带钢轧后板形质量并影响下游工艺进程。板形执行机构是实现板形控制的关键工艺手段,较低的机构模型精度会影响板形偏差的在线修正能力并易于产生板形缺陷。...冷轧带钢板形控制技术是钢铁工业领域的核心控制技术之一,其控制效果直接决定了带钢轧后板形质量并影响下游工艺进程。板形执行机构是实现板形控制的关键工艺手段,较低的机构模型精度会影响板形偏差的在线修正能力并易于产生板形缺陷。本文综合考虑轧制力、带钢几何尺寸等关键轧制参数对板形的影响规律,以及不同板形执行机构间的耦合作用机制,建立了包含二次影响系数的弯辊-轧辊倾斜协同设定模型;引入“过喷系数”概念,构建了考虑相邻乳化液喷嘴间部分流量重叠特性的分段冷却流量控制模型;为提升设定模型的精度和计算效率,基于启发式搜索理论制定了设定模型中各系数的寻优设置策略。以某厂1 450 mm UCM五机架六辊冷连轧机组为应用平台开展工业试验,结果表明,本文开发的板形执行机构设定模型及设定策略可高效、准确地修正在线板形偏差,降低了设定模型的设置难度,并使板形均方根误差命中率提升了约8%。展开更多
基金financially supported by the National Key Research and Development Program Project(Grant No.2023YFB3710200)the Fundamental Research Funds for the Central Universities(Grant No.06930007).
摘要Transverse thickness difference is a key indicator for evaluating the quality of cold-rolled silicon steel products,jointly determined by hot-rolled silicon steel data and cold rolling process parameters.However,there are“process barriers”and“data islands”between different production lines,resulting in low accuracy and poor interpretability.In addition,the transverse thickness difference mainly relies on manual sampling measurement.The lag and uncertainty of the measurement results lead to a lack of effective online control methods.To overcome this,a cold-rolled silicon steel transverse thickness difference control framework based on interpretable machine learning is proposed.First,a hot-cold rolling cross-process data platform is established to match and integrate multivariate data from different production lines,providing a data foundation.Then,an RUN-HLSSVM-AdaBoost model prediction model combining Runge-Kutta algorithm optimized hybrid kernel least squares support vector machine and AdaBoost ensemble modeling method is established.Afterward,the adaptive bandwidth kernel density estimation improved by local weighting strategy is used to construct a prediction interval,which characterizes the uncertainty of the prediction results.SHAPley Additive exPlanations interpretable method is used to break the“black box”limitation and reveal the influence of hot and cold rolling parameters on the transverse thickness difference,and finally,an online control strategy is proposed.Industrial experiments have verified the effectiveness of the above framework,and the transverse thickness difference has been significantly improved,which provides a new paradigm for solving the problem of online control of transverse thickness difference of cold-rolled silicon steel.
摘要冷轧带钢板形控制技术是钢铁工业领域的核心控制技术之一,其控制效果直接决定了带钢轧后板形质量并影响下游工艺进程。板形执行机构是实现板形控制的关键工艺手段,较低的机构模型精度会影响板形偏差的在线修正能力并易于产生板形缺陷。本文综合考虑轧制力、带钢几何尺寸等关键轧制参数对板形的影响规律,以及不同板形执行机构间的耦合作用机制,建立了包含二次影响系数的弯辊-轧辊倾斜协同设定模型;引入“过喷系数”概念,构建了考虑相邻乳化液喷嘴间部分流量重叠特性的分段冷却流量控制模型;为提升设定模型的精度和计算效率,基于启发式搜索理论制定了设定模型中各系数的寻优设置策略。以某厂1 450 mm UCM五机架六辊冷连轧机组为应用平台开展工业试验,结果表明,本文开发的板形执行机构设定模型及设定策略可高效、准确地修正在线板形偏差,降低了设定模型的设置难度,并使板形均方根误差命中率提升了约8%。