摘要
采用PCBN刀具进行高速硬车削AISI P20淬硬钢的切削试验,并通过方差分析研究切削速度、进给量、切削深度和刀尖圆弧半径对切削力的影响。基于获得的试验数据,应用人工神经网络方法建立高速硬车削P20淬硬钢时的切削力预测模型。试验与仿真分析显示,切削力随进给量、切削深度和刀尖圆弧半径的增加而增大,而不同切削速度下的切削力值几乎保持不变;同时,切削深度对切削力的影响最为显著,其次为进给量,再次为刀尖圆弧半径,而切削速度的影响则非常微弱。
Experimental investigation of cutting force was performed in high speed turning of hardened AISI P2O steel with PCBN tool.The influence of cutting speed,feed rate ,depth of cut and nose radius on cutting force were assessed using the analysis of variance （ ANOVA ）.Due to the complexity of machining process,based on the required experimental data, artificial neural network was employed to develop the predictive model of cutting force.Experiments and simulations showed that increased feed rate ,depth of cut and nose radius led to higher cutting force,while the cutting force under different cutting rate remains the same.A t the same time ,depth of cut is found the most influential parameter on cutting force followed by feed rate and corner radius. However the contribution of cutting speed is very slight.
出处
《机械设计与制造》
北大核心
2012年第9期175-177,共3页
Machinery Design & Manufacture
关键词
切削力
高速加工
硬车削
淬硬钢
神经网络
Cutting Force
High-Speed Machining
Finish Hard Turning
Hardened Steel
Neural NetwOrk
