摘要
通过对挤出吹塑工艺过程建立了成形工艺参数与制品质量关系的RBF(径向基函数)级联型神经网络,经训练,该网络可预测各成形工艺参数对制品质量的影响,从而可实现对制品质量的有效控制,并实验验证了其正确性和可行性。
RBF （radical basis function） serial neural networks for two stages of extrusion blow molding （parison formation and inflation included） are created to build up the relationship between process parameters and part quality. These networks can predict the effects process parameters on part quality after training. Consequently, parts quality can be effectively controlled. The experiment shows that the networks are validity and feasibility.
出处
《塑性工程学报》
CAS
CSCD
北大核心
2006年第6期99-102,共4页
Journal of Plasticity Engineering
