Abstract：The content of lead in crude tin relates directly to the production efficiency of the solder vacuum furnace. In order to change the current status that the content of lead in crude tin can be only determined by manual testing, the soft measurement models based on algorithm principles of back-propagation neural network (BPNN) and generalized regression neural network (GRNN) were constructed. The predicted results of two models were compared and analyzed. The results showed that the soft measurement model of lead content in crude tin based on GRNN exhibited higher prediction accuracy. Meanwhile, the monitoring system of lead in crude tin was successfully developed using the Matlab Script node technology in LabVIEW, realizing the real-time on-line soft prediction of lead content in crude tin by soft measurement model based on BPNN and GRNN. The running results indicated that the developed monitoring system was stable and reliable.
李家文,赖华,冯丽辉. 基于神经网络与LabVIEW平台建立焊锡真空炉粗锡含铅量的软测量模型与在线监测研究[J]. 冶金分析, 2017, 32(2): 1-6.
LI Jia-wen, LAI Hua, FENG Li-hui. Research on soft measurement model and on-line monitoring of lead content in crude tin in solder vacuum furnace based on neural network and LabVIEW. , 2017, 32(2): 1-6.
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