The monitoring of cutting tool wear has a great significance for the processing quality and stability. To overcome the difficulty to reflect all the wear mechanisms for the monitoring method based on finite element model or the excessive dependence on data extraction quality for the monitoring method based on sensor data, this paper proposes a model and data fusion method based on particle filter algorithm. Two different kinds of materials AISI 1045 and AISI 4340 are chosen to carry out the turning experiments. The mean absolute percentage error (MAPE) of the fusion method is 0.6%~4.1% lower and the coefficient of determination (R 2 ) is more close to 1 compared with the finite element model method or sensor data method individually. Experimental results verify the feasibility and the superiority of the fusion method.

中文翻译：

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基于粒子滤波算法的车削刀具磨损融合监测方法研究


刀具磨损的监测对于加工质量和稳定性具有重要意义。针对基于有限元模型的监测方法难以反映所有磨损机理或基于传感器数据的监测方法过分依赖数据提取质量的问题，提出一种基于粒子滤波算法的模型与数据融合方法。选择两种不同的材料AISI 1045和AISI 4340进行车削实验。与单独的有限元模型方法或传感器数据方法相比，融合方法的平均绝对百分比误差（MAPE）降低了0.6%~4.1%，决定系数（R 2 ）更接近1。实验结果验证了该融合方法的可行性和优越性。