Feature Extraction of Lathe Tool Crater Wear Image Using Auto-Encoder

To monitor tool wear during cutting processing, tool wear is mainly measured indirectly through sensor signals that are most correlated with wear. The direct measurement method of tool wear using image and optical sensors is more accurate than indirect measurement, but it is mainly used to measure the amount of wear because it is difficult to apply in real time. Existing studies have been conducted mainly on flank wear caused by friction with workpiece. On the other hand, crater wear is an important monitoring factor because it is caused by friction with chips generated during processing and causes sudden tool breakage. However, for crater wear, it is difficult to measure the amount of wear because the indicator of the amount of wear is depth. Therefore, although image processing-based studies have been conducted to measure the amount of crater wear, there is a clear limit to accurately measure the depth only with the image on the top of the tool. In this work, we propose a method to extract unique features of crater wear images through autoencoder, a deep learning technique, and use them as a new measure of wear.