Neural network approach for automatic image analysis of cutting edge wear

- We have alleviated the requirement of manual image segmentation, allowing us to extend from recognizing only a couple of wear categories to the linear tool's life index.
- Our results show a good correlation between the new methods and the commonly used VB index measured optically, with the absolute mean relative error in the range of 6.7% over the entire lifetime of the tools.
- An increase in the number of the neigborhood makes it possible to eliminate error in the classification of pixels in the wear zone.

This study describes image processing systems based on an artificial neural network to estimate tool wear. The Single Category-Based Classifier neural network was used to process tool image data. We present a method to determine the rate of tool wear based on image analysis, and discuss the evaluation of errors. Using the proposed algorithm, we made in Visual Basic the special Neural Wear software for analysis of the worn part of the cutting edge. For example, the image of worn edge was created determining the optimum setting of Neural Wear software to automatically indicate the wear area. The result of the analysis was the number of pixels that belonged to the worn area. Using these settings, we made an image analysis of edge wear for different working times. We used the calculated parameters of correlation between the number of pixels and VB index. Our results promise a good correlation between the new methods and the commonly used optically measured VB index, with an absolute mean relative error of 6.7% for the tools' entire life range. Automatic detection of wear of the cutting edge can be useful in many applications; for example, in predicting tool life based on the current value of edge wear.