Abstract
In order to assess the degree of wear of tool for milling process quantitatively, a new assessment approach is proposed. Firstly, making full use of the neighbor information, two sensitive features are selected by using the neighborhood rough set model, and then, boundary curves are established by using the nearest neighbor model with noncounter data in two dimension spaces. Secondly, the intersection area or expanding area is used to describe the difference between two boundary models because the intersection area or expanding area can consider the effect of distance and angle simultaneously in two dimension spaces. Thirdly, after determining a baseline state, a new quantitative assessment indicator (QAI) can be calculated based on the intersection area or expanding area. The QAI can directly measure the difference between the model of baseline state and the model of unknown state and indirectly measure the degree of wear of tool. Finally, the effectiveness of the assessment approach is proven by using the Milling Dataset which was provided by the NASA Ames Research Center.
Highlights
In order to guarantee the quality and productivity of the machining process, tool condition monitoring has received more and more attention
After selecting sensitive features by using the neighborhood rough set model, the wear condition of tool could be described by using the boundary curves which are trained from the nearest neighbor model
In order to evaluate the degree of tool wear quantitatively, two new assessment indicators quantitative assessment indicator (QAI) are calculated for Case 1 and Case 2, as shown in Figures 13 and 20
Summary
In order to guarantee the quality and productivity of the machining process, tool condition monitoring has received more and more attention. Nouri et al [5] proposed a method to monitor end milling tool wear in real-time by tracking the force model coefficient during the cutting process to predict the life to tool. Data-driven models tried to model or simulate the tool wear process by using the actual running data, including force, vibration, acoustic emission, spindle motor and feed currents, and so on. In order to monitor the wear condition of tool in process, a new assessment method based on the neighborhood rough set model and the nearest neighbor model is proposed. After selecting sensitive features by using the neighborhood rough set model, the wear condition of tool could be described by using the boundary curves which are trained from the nearest neighbor model. The quantitative assessment indicator is designed based on the areas which are created by the boundary curves in the two-dimension spaces
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