摘要
文中广泛收集和整理企业第一线的焊接工艺和焊接接头力学性能数据,并建立起相关数据库.应用遗传算法优化BP神经网络,建立焊接接头力学性能预测模型,实现碳钢、低合金高强钢以及不锈钢的抗拉强度、屈服强度、断后伸长率以及断面收缩率等力学性能指标预测.结果表明,材料成分和焊接工艺为影响接头力学性能的主要参数,应用遗传算法优化BP神经网络建立焊接头力学性能预测模型,可以达到较理想的预测精度.
In this paper,A large number of production line data was collectd and collated on welding technology and mechanical properties of welded joints.A relevant database was established based on these data,then BP neural networks optimized by genetic algorithm was used to mock up these mechanical properties of welding joint for prediction.By which mechanical properties of tensile strength,yield strength and elongation of carbon steel,low alloy high strength steel and stainless steel can be predicted.The results show that the material composition and welding technology as the main parameters of joints can be determined by this system and staisfied prediction accuracy can be obtained as well.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2011年第7期105-108,118,共4页
Transactions of The China Welding Institution
关键词
神经网络
遗传算法
力学性能预测
neural network
genetic algorithm
prediction of mechanical properties
