针对表面粗糙度和刀具振幅的切削用量优化研究

对进给量、切削速度和轴向切深这3个切削参数对工件表面粗糙度和刀具振动幅度的影响进行试验研究。采用BBD响应面法对6061铝工件进行端铣加工试验,并通过数学建模对试验结果进行分析。提出一种基于遗传算法的多目标优化方法来同时减小工件表面粗糙度和刀具振动幅度。建立能预报表面粗糙度和刀具振动的径向基神经网络模型,并通过试验验证其准确性。

Research on Optimization of Cutting Dosage for Surface Roughness and Tool Vibration Amplitude

The effects of three cutting parameters, such as feed rate, cutting speed and axial depth of cut, on the surface roughness of the workpiece and the vibration amplitude of the tool were studied experimentally.The end milling test for 6061 aluminum workpiecewas carried out by BBD response surface method, and the experimental results were analyzed by mathematical modeling. A multi objective optimization method based on genetic algorithm was proposed to reduce the surface roughness and tool vibration amplitude. A radial basis neural network model for predicting surface roughness and tool vibration was established and its accuracy was verified by experiments.