Research on Fusion Monitoring Method of Turning Cutting Tool Wear Based on Particle Filter Algorithm

Abstract

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 (R2) 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.

Keywords

• Tool wear monitoring
• particle filter algorithm
• model and data fusion
• finite element model
• sensor data