摘要
采用有限元数值模拟、正交实验设计与智能优化算法和生产相结合的方法,针对连接杆头的模锻成形工艺进行了优化研究,根据模具磨损量与其影响因素之间的映射关系,以试验数据为基础,建立了BP神经网络模型。模型的输入包括坯料温度、模具温度和摩擦系数等工艺参数,输出为模具磨损量;运用该模型对模具磨损量能进行了预测,通过试验数据对模型的预测精度进行了可靠性验证。通过智能优化算法,确定了连接杆头的工艺参数:坯料温度1179℃,模具温度368℃,摩擦系数0. 17。采用上述工艺参数进行了连接杆头的模锻成形,模具寿命得到提高。
The die forging process of the connecting rod head was optimized by means of the finite element numerical simulation,orthogonal experiment design,and the intelligent optimization algorithm and actual production.Considering the mapping relationship between wear amount of the mold and its influence factors,the BP neural network model was established based on the experimental data.The inputs of neural network model included the process parameters such as billet temperature,mold temperature and friction coefficient,and the output was the wear amount of mold.The established model was used to predict the wear amount of mold,and the reliability of the model was verified by the experiments.Using the intelligent optimization algorithm,the process parameters of the connecting rod head were determined as follows:the billet temperature is 1179℃,the mold temperature is 368℃,and the friction coefficient is 0.17.The die forging forming of the connecting rod head was carried out with the above process parameters,and the mold life is improved.
作者
王相钧
王大勇
王培涛
张振
周蓬磊
WANG Xiang-jun;WANG Da-yong;WANG Pei-tao;ZHANG Zhen;ZHOU Peng-lei(Provincial Key Laboratory of Key Material Laboratory for Railway,Dalian Jiaotong University,Dalian 116028,China;Shanxi Fast Gear Co.,Ltd.,Baoji 722409,China;Dalian Locomotive&Rolling Stock Co.,Ltd.,Dalian 116022,China)
出处
《塑性工程学报》
CAS
CSCD
北大核心
2019年第6期36-41,共6页
Journal of Plasticity Engineering
关键词
连接杆头
模锻
智能算法
工艺参数
优化研究
connecting rod head
die forging
intelligent algorithm
process parameters
optimization research
