人工神经网络对管材张减精度预测  

PREDICTION OF ACCURACY OF STRETCH REDUCTION BY ARTIFICIAL NEURAL NETWORKS

作　　者：双远华[1] 樊建成[1] 赖明道[1] 

机构地区：[1]燕山大学

出　　处：《钢铁》2000年第2期28-31,共4页Iron and Steel

基　　金：国家重点科技项目! (攻关 )计划 ( 95 5 2 8 0 2 0 2 0 2 E)

摘　　要：无缝钢管张力减径是多机架机组对荒管进行无芯棒连轧过程。它具有复杂的金属流动状态,影响其精度的工艺参数较多,难以用现有轧制理论进行分析。应用人工神经网络的BP算法,对不同坯料样本进行训练学习,确定其权值和阈值,建立起预测管材精度的数学模型。经试验证明该模型具有较高的可靠性和精度。Stretch reduction of seamless tubes is a continuous rolling process for blank tubes without mandral It is a complicated process of metal flows and influenced by many technical parameters, so is difficult to be analyzed This paper describes the prediction of accuracy of stretch reduction with use of the back propagation algorithm of neural networks Connection weights and thresholds are determined through learning and modifying each samples It is proved that the models established have certain reliability and precision

关 键 词：张力减径 人工神经网络 精度 数学模型 无缝钢管 

分 类 号：TG33[金属学及工艺—金属压力加工]