论文部分内容阅读
通过对鞍钢 16 80 mm冷连轧机力能参数、结构参数、带钢厚度和板形精度的大型综合测试 ,采用综合遗传神经网络建立轧制力模型 ,与实测误差不大于 5 % ;采用综合改进遗传算法与冷连轧机辊型系统优化相结合 ,板形废品率从 2 .2 4%降至 1.19% ;运用综合改进遗传算法与冷连轧机大系统参数相结合优化轧制规程 ,相同厚度来料成品由 1m m扩大至 0 .8m m;运用“机电结合故障诊断法”,找出带钢厚差无法消除和易断带的原因。综合措施提高了轧机的效能和产品的精
Through the large-scale comprehensive test on the energy parameters, structural parameters, strip thickness and plateform accuracy of 16 80 mm tandem cold rolling mill in Ansteel, the rolling force model was established by using the integrated genetic neural network with the measured error of not more than 5%. The comprehensive improvement The combination of genetic algorithm and roller system optimization of cold tandem mill reduced the reject rate of plate form from 2.24% to 1.19%. By combining the improved genetic algorithm and large system parameters of tandem cold mill, the rolling schedule and the same thickness Finished products from 1m m expanded to 0 .8 m m; use of “mechanical and electrical fault diagnosis combined with” to find out the thickness of the strip can not be eliminated and easy to break with the reasons. Comprehensive measures to improve the mill’s performance and product refinement