面向低碳低噪的螺纹车削工艺参数优化  

Cutting parameters optimization of thread turning oriented to low carbon and low noise

作　　者：张雷[1] 张北鲲 鲍宏[1] ZHANG Lei , ZHANG Beikun , BAO Hong(School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, Chin)

机构地区：[1]合肥工业大学机械工程学院,安徽合肥230009

出　　处：《计算机集成制造系统》2018年第3期639-648,共10页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(51575152)~~

摘　　要：为实现螺纹车削加工的低噪以及低碳化,利用正交实验和广义回归神经网络建立了螺纹车削过程噪声目标函数和包含耗电碳排放、刀具碳排放、切削液碳排放的碳排放目标函数。考虑加工过程中机床特性和加工质量的实际约束条件,建立以切削速度和进给量为优化变量,碳排放和噪声为优化目标的多目标优化模型。引入权重系数将其转化为单目标优化模型,最后利用自适应小生境遗传算法对优化模型进行优化求解,优化结果表明噪声和碳排放间呈负相关关系,在碳排放中耗电碳排放所占比例最大。To realize the low carbon and low noise in thread turning process,the noise objective function model and the carbon emissions objective function model included electric power carbon emissions,cutting tool carbon emissions,cutting fluid carbon emissions based on orthogonal experiment and General Regression Neural Network（GRNN）were established.By considering the constraints of machine tool specification and workpiece surface quality,a multi-objective optimization model was established,which took the cutting speed and feed as the optimization variables.The weight coefficients were introduced to transform multi-objective optimization model into single objective optimization model.A self-adaptive niching genetic algorithm was applied to solve the single objective optimization model,and the results showed that noise and carbon emissions presented apparent negative correlativity and electric power carbon emissions was the biggest part in carbon emissions.

关 键 词：低碳低噪 螺纹车削 切削参数 多目标优化 自适应小生境遗传算法 

分 类 号：TH16[机械工程—机械制造及自动化]