摘要
针对高体积分数碳化硅颗粒增强型铝基复合材料(SiC_(p)/Al)在铣削过程中加工难度大、表面质量差等问题,提出了纵扭超声振动辅助铣削复合工艺。以超声振幅、切削速度、每齿进给量和切削深度为变量,设计了四因素五水平正交试验。通过响应曲面法和人工神经网络,建立了切削力、切削温度和表面粗糙度的预测模型,分析了4个参数变量中两个指标的交互影响作用,并对预测模型的准确性进行了对比验证。最后,采用遗传算法对切削力、切削温度和表面粗糙度进行了多目标参数优化。结果表明,响应曲面法与人工神经网络建立的模型均有较好的预测能力,但人工神经网络准确性更高。采用遗传算法优选出的最佳参数组合为超声振幅A=1.84μm,切削速度v_(c)=20 m/min,每齿进给量f_(z)=0.015 mm/z,切削深度a_(p)=0.8 mm,经过验证试验后发现,采用优选参数有效降低了切削力、切削温度和表面粗糙度,各值分别为切削力F_(t)=7.23 N,切削温度T=40.18℃,表面粗糙度R_(a)=2.4673μm,预测误差分别为6.91%、6.53%、2.53%,证明了预测模型的准确性与优化参数的有效性。
In order to solve the problems of high volume fraction silicon carbide particle reinforced aluminum matrix composite(SiC_(p)/Al)machining difficulty and poor surface quality,the longitudinal torsional ultrasonic vibration assisted milling composite process was proposed.Taking ultrasonic amplitude,cutting speed,feed per tooth and cutting depth as variables,a four-factor and five-level orthogonal experimental study was designed.By using response surface method and artificial neural network,the prediction models of cutting force,cutting temperature and surface roughness are established,the interaction effect of two indexes among the four parameter variables is analyzed,and the accuracy of the prediction models is compared and verified.Finally,the multi-objective parameters of cutting force,cutting temperature and surface roughness are optimized by genetic algorithm.The results show that both the response surface method and the artificial neural network have better predictive ability,but the artificial neural network is more accurate.The optimal parameter combination optimized by genetic algorithm is ultrasonic amplitude A=1.84μm,cutting speed v_(c)=20 m/min,feed per tooth f_(z)=0.015 mm/z,cutting depth a_(p)=0.8 mm.After verification experiment,it is found that the optimal parameter can effectively reduce the cutting force,cutting temperature and surface roughness,and the values are F_(t)=7.23 N,T=40.18℃,R_(a)=2.4673μm,respectively.And the prediction errors were 6.91%,6.53%and 2.53%,respectively,which proved the accuracy of the prediction model and the effectiveness of the optimization parameters.
作者
牛秋林
戴福朋
荆露
王星华
刘俐鹏
肖玉斌
NIU Qiulin;DAI Fupeng;JING Lu;WANG Xinghua;LIU Lipeng;XIAO Yubin(Hunan University of Science and Technology,Xiangtan 411201,China;Jianglu Machinery Electronics Group Co.,Ltd.,Xiangtan 411100,China)
出处
《航空制造技术》
CSCD
北大核心
2024年第12期14-26,共13页
Aeronautical Manufacturing Technology
基金
国家自然科学基金面上项目(52075168)
湖南省教育厅重点项目(22A0331)。
关键词
纵扭超声振动辅助铣削
响应曲面法
人工神经网络
遗传算法
预测模型
多目标参数优化
Longitudinal-torsional ultrasonic vibration assisted milling
Response surface method
Artificial neural network
Genetic algorithm
Prediction model
Multi-objective parameter optimization
