Integrating object detection and image segmentation for detecting the tool wear area on stitched image

Abstract

Flank wear is the most common wear that happens in the end milling process. However, the process of detecting the flank wear is cumbersome. To achieve comprehensively automatic detecting the flank wear area of the spiral end milling cutter, this study proposed a novel flank wear detection method of combining the template matching and deep learning techniques to expand the curved surface images into panorama images, which is more available to detect the flank wear areas without choosing a specific position of cutting tool image. You Only Look Once v4 model was employed to automatically detect the range of cutting tips. Then, popular segmentation models, namely, U-Net, Segnet and Autoencoder were used to extract the areas of the tool flank wear. To evaluate the segmenting performance among these models, U-Net model obtained the best maximum dice coefficient score with 0.93. Moreover, the predicting wear areas of the U-Net model is presented in the trend figure, which can determine the times of the tool change depend on the curve of the tool wear. Overall, the experiments have shown that the proposed methods can effectively extract the tool wear regions of the spiral cutting tool. With the developed system, users can obtain detailed information about the cutting tool before being worn severely to change the cutting tools in advance.

A review of traditional image segmentation methods

“…As shown in the following figure, Figure 1 is the original image, Figure 2 is the image after fixed threshold processing. Experiments determined that the effect was better when the value of treshold was 127 [5].…”

A Novel Denoising Autoencoder Method for Surface Defect Detection of Screw Products

“…To restore a corrupted image as closely as possible to the input, the model is trained by using mean square error. The mean square error is shown in equation (4), equation ( 5) and equation (6). Encoders and decoders are defined in equation ( 7) and equation (8).…”

“…The supervised-based deep learning technique is capable of extracting useful features automatically from large amounts of new data, making it more suitable for detecting defects in industrial manufacturing [1]. It is not possible to implement supervised learning due to the requirement of a large number of defective samples for model training [2][3][4][5][6]. Unsupervised learning eliminates the weakness of supervised learning by only training normal images [7][8][9][10][11][12].…”

Study on In-Situ Tool Wear Detection during Micro End Milling Based on Machine Vision

“…Li et al [ 11 ] established an automatic tool wear image detection system to segment the wear images by finding the optimal threshold value through maximum inter-class variance and iteration, and counted the wear interval pixels to calculate the tool wear quantization value. Lin et al [ 12 ] used machine vision and machine learning based methods to segment and identify tool wear areas and have achieved detection of tool wear status.…”