摘要
钠硫电池中含有大量的高纯度钠,在自动化拆解过程中存在燃烧、爆炸等安全风险。针对钠硫电池在车削拆解时存在的安全性问题,提出一种改进SSA-BP神经网络算法来预测刀具加工的最高温度。利用ABAQUS软件计算出刀具加工的实时温度,通过电池拆解实验验证仿真数据的可靠性;然后以仿真温度数据建立样本,利用Tent混沌映射对SSA-BP神经网络算法进行优化,建立刀具温度仿真预测模型。实验结果表明:该仿真预测模型收敛速度快,鲁棒性强,模型误差小。
Sodium sulfur batteries contain a large amount of high-purity sodium,which leads safety risks such as combustion and explosion during the automated disassembly process.A modified SSA-BP neural network algorithm was proposed to predict the maximum temperature of tool processing in response to the safety issues of sodium sulfur batteries during turning and disassembly.The real-time temperature of tool machining was calculated using ABAQUS software,and the reliability of the simulation data was verified through battery disassembly experiments.Then,samples were established based on simulated temperature data,and the SSA-BP neural network algorithm was optimized using Tent chaotic mapping to establish a tool temperature simulation prediction model.The experimental results show that the simulation prediction model has fast convergence speed,strong robustness,and small model error.
作者
屈朝阳
胡光忠
王平
薛祥东
QU Zhaoyang;HU Guangzhong;WANG Ping;XUE Xiangdong(School of Mechanical Engineering,Sichuan University of Science&Engineering,Yibin Sichuan 644000,China;Panzhihua Advanced Manufacturing Technology Key Laboratory,Panzhihua Sichuan 617000,China)
出处
《机床与液压》
北大核心
2024年第9期100-107,127,共9页
Machine Tool & Hydraulics
基金
攀枝花市先进制造技术重点实验室开放基金(2022XJZD012022XJZD01)
过程装备与控制工程四川省高校重点实验室开放基金(GK202205)
四川省科技计划项目(2022SZYZF07)。
