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Comprehensive prediction of surface quality of hot-rolled strip with multiple and single defects |
SUN Jian-liang1, SUN Meng-qian1, GUO He-song1, JI Jiang2, XU Li-pu2, PENG Yan1 |
1. National Engineering Research Center for Equipment and Technology of Cold Rolled Strip,Yanshan University, Qinhuangdao 066004, Hebei, China; 2. China Nation Heavy Machinery Research Institute Co., Ltd., Xi′an 710032, Shaanxi, China |
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Abstract In view of the problem that the surface quality defects of the hot-rolled strip are serious,have many types and different mechanisms,and cannot be diagnosed online. A method for the comprehensive diagnosis of surface quality problems of the hot-rolled strip with multiple defects and single defects was presented by combining the deep belief network model of each dimension reduction layer. Considering the traditional deep belief network model in the shortfalls,layer number and the number of nodes were put forward and set up the integration of each dimension reduction layer belief network model,nodes were reduced due to network algorithm and the layer number of forecasting error caused by uncertainty,and used the correlation between classic deep belief network forecasting results,and the forecasting results of dimension reduction layer integration was the only anticipation value. In view of the problem of surface quality multiple defects in the hot-rolled strip production,considering the different input parameters of different surface quality defects,a comprehensive diagnosis strategy for surface quality multiple defects and single defects of the hot-rolled strip was established. In a typical surface become warped in the process of hot rolling production line production leather,metal surface quality defects such as seal and burr problem,application integration of each dimension reduction deep belief network model and multiple flaws and single comprehensive diagnosis strategy,the results show that the surface quality defects of the model prediction accuracy were above 80%,for the hot-rolled strip surface defects with good diagnosis effect.
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Received: 08 May 2020
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