【作者】 孙建新；

【导师】 李斌；

【作者基本信息】 华中科技大学 ， 机械电子工程， 2007， 硕士

【摘要】 数控加工过程中,由于工件余量不均匀、材料硬度不等、刀具磨损变化等因素影响,一般选用保守的加工速度,机床应有效能不能充分发挥。对加工过程中的切削力进行实时监测并自适应调节,是提高加工效率的重要途径。切削力直接测量一般采用三维测力仪,这种方法受条件限制,只适用于实验室研究,很难应用于实际加工过程。因此,本文提出了基于进给伺服电机电流的铣削力间接测量方法,并从以下几个方面进行了研究:通过对铣削加工过程中铣削力传递和机电转换过程分析,得出铣削力和进给伺服电流这两个铣削过程的首尾参量之间存在联系。对切削实验中采集到的铣削力信号进行时域、频域分析,提取铣削力信号特征值。分析进给伺服电流信号时域、频域特性时,采用小波分析方法提取与铣削力频率相同的电流信号,以此信号幅值的平均值作为进给伺服电流信号表征铣削力的特征值。针对进给伺服电流与铣削力的关系具有高度非线性的特点,采用神经网络BP算法,以进给电机电流信号的特征值、进给速度、主轴转速为输入,以铣削力为输出,通过大量实验样本的训练,建立了进给伺服电流和铣削力的关系模型,并通过优化神经网络参数,建立了一个符合要求的基于进给电机电流信号的铣削力预测神经网络模型。最后,通过建立的铣削力和进给伺服电流检测实验平台,进行了几组典型的切削实验,对模型性能和预测精度进行了验证,并分析影响预测模型精度的主要因素。最终验证了模型的有效性。更多还原

【Abstract】 During NC machining, the factors such as change in the depth of cut, material hardness variation and tool wear may result in the conservative feed rate selected and therefore insufficient use of NC machining tools. It is a important way to increase productivity by real-time monitoring and adaptive adjusting the machining process.The method that the cutting force is measured directly by the 3-D dynamometer is only used in laboratory, can’t be used in industry. As a result, an indirect milling force measurement method based on feed motor current has been proposed. The dissertation focuses on the following research work.Analyzing the transfer of milling force and the process of power conversion between mechanical and electrical power particularly, the relation of the milling force and feed motor current has been proved. Using the data from the experiments to analyze the milling force signal in both time and frequency domain, extracting the value of the milling force. Analyzing the feed motor current signal in both time and frequency domain, using the wavelet analysis method to extracting the current which has the same frequency as the milling force. The average value of this transformed current is used to be the value of feed motor current in token of milling force.Because there is extraordinary non-linear relation between feed motor current and milling force, the BP arithmetic of artificial neural network method is used to establish the model of the relation. The value of feed motor current, feed speed and spindle rotate speed are used to be the input of the model, the milling force is used to be the output. After training by a lot of experimental data, optimizing the parameters of the net, a neural network model which is adapted to forecast milling force base on feed motor current has been established.At last, using the experimental platform which is established to measure the milling force and feed motor current, taking several representative experiments to validate the capability and precision of this model, and analyzing the factors which have an effect on the model. Finally the validity of the prediction model has been proved.更多还原