摘要
该文介绍一种基于改进深度强化学习的铣削参数优化方法,用于改变传统铣削加工参数由操作员经验选择,节能和经济性缺乏关注的现状,提高加工过程中的能源效率,减少能源消耗和成本。采用APSO和NSGA-II的组合算法,以VDL-850A为载体,使用Matlab编程,比较研究多目标优化模型与单目标优化模型。结果显示,以低加工成本为优化目标进行分析,铣削速度较低,主轴转速慢,刀具磨损小,加工成本低;以节能为优化目标进行分析,铣削速度和主轴转速更快,加工时间更短,加工能量消耗更少。
This paper introduces an optimization method of milling parameters based on improved deep reinforcement learning,which is used to change the current situation that traditional milling parameters are selected by operators'experience,lack of attention to energy saving and economy,and improve energy efficiency in the machining process.reduce energy consumption and costs.Using the combined algorithm of APSO and NSGA-II,taking VDL-850A as the carrier and programming with Matlab,the multi-objective optimization model and the single-objective optimization model are compared and studied.The results show that taking the low machining cost as the optimization goal,the milling speed is lower,the spindle speed is slow,the tool wear is small,and the machining cost is low;taking energy saving as the optimization goal,the milling speed and spindle speed are faster,the processing time is shorter,and the processing energy consumption is less.
出处
《科技创新与应用》
2023年第23期23-26,30,共5页
Technology Innovation and Application
基金
广安市科技创新项目(2021GYF01)。
关键词
铣削参数
强化学习
优化目标
节能
经济性
milling parameters
reinforcement learning
optimization goal
energy saving
economy
