方坯裂纹敏感钢种在连铸过程中其角部容易出现裂纹,为了保证连续化生产与解决产品质量问题,建立了铸坯的数字化模型及质量分析算法,形成了一套可靠高效的分析系统.系统通过在线采集高频时序数据与质量数据实现米级跟踪落位,动态地计算工艺变量的稳定性,基于随机森林(random forest,RF)和费舍尔线性判别(Fisher's linear discriminant analysis,FDA)开发改进的自组织映射算法(self organizing map,SOM),通过变量筛选降维与稳定性判据建立因子模型,实现高维数据压缩的同时保留其空间拓扑结构并投影至二维平面进行可视化,实现角裂风险计算与工艺生产路径的动态跟踪,模型预测的准确率保持在90％以上,实现了工艺优化与在线监控.系统自投用以来,典型钢种的角裂发生率由35.3％下降至8.3％.
Development and application of a corner crack analysis system for electric furnace billet
Sensitive steel grades with billet cracks are prone to cracking at the corners during continu-ous casting.In order to ensure continuous production and solve product quality problems,a digital model and quality analysis algorithm for the casting billet have been established,forming a reliable and efficient analysis system.System achieves meter level tracking and positioning through online col-lection of high-frequency time series data and quality data,dynamically calculating the stability of process variables,an improved self organizing map(SOM)algorithm was developed based on random forest(RF)and Fisher's linear discrimination analysis(FDA).The algorithm establishes a factor model through variable screening,dimensionality reduction,and stability calculation,achieving high-dimensional data compression while preserving its spatial topology structure and projecting it onto a two-dimensional plane for visualization,achieving dynamic tracking of corner crack risk rating and process production path.The accuracy of model prediction is maintained at over 90％,achieving process optimization and online monitoring.Since the system was put into use,the incidence of corner cracks in typical steel grades has decreased from 35.3％to 8.3％.
万方数据
维普
