Prediction of cutting temperature in the milling of wood-plastic composite using artificial neural network
Prediction of cutting temperature in the milling of wood-plastic composite using artificial neural network
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DOI：
10.15376/biores.16.4.6993-7005
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发表时间：
2021-09
期刊：
影响因子：
1.5
通讯作者：
Feng Zhang;Zhanwen Wu;Yong Hu;Zhaolong Zhu;Xiaolei Guo
中科院分区：
文献类型：
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作者：
Feng Zhang;Zhanwen Wu;Yong Hu;Zhaolong Zhu;Xiaolei Guo
在木塑复合材料铣削加工中，切削温度对刀具寿命和切削质量有很大影响。利用红外测温技术分析了切削参数对切削区切削温度的影响。结果表明，切削温度随主轴转速和切削深度的增加而升高，随进给量的增加而降低。此外，基于实验数据，建立了切削温度的BP神经网络预测模型。试验数据的R2值为0.97354，表明所建立的模型具有较高的预测精度。研究结果对切削温度的预测和控制具有指导作用，对提高刀具寿命、加工质量和加工效率具有重要意义。
In the milling of wood-plastic composites, the cutting temperature has a great influence on tool life and cutting quality. The effects of cutting parameters on the cutting temperatures in the cutting zone were analyzed using infrared temperature measurement technology. The results indicated that the cutting temperature increased with the increase of spindle speed and cutting depth but decreased with the increase of feed rates. In addition, based on experimental data, a BP neural network model was proposed for predicting the cutting temperatures. The value of R2 was 0.97354 for the testing data, which indicates that the developed model achieved high prediction accuracy, respectively. The results of the study can play a guiding role in the prediction and control of cutting temperature, which is of great importance in the improvement of tool life, machining quality, and machining efficiency.