Cutting Edge Localisation in an Edge Profile Milling Head

Abstract

Abstract. Wear evaluation of cutting tools is a key issue for prolonging their lifetime and ensuring high quality of products. In this paper, we present a method for the effective localisation of cutting edges of inserts in digital images of an edge profile milling head. We introduce a new image data set of 144 images of an edge milling head that contains 30 inserts. We use a circular Hough transform to detect the screws that fasten the inserts. In a cropped area around a detected screw, we use Canny's edge detection algorithm and Standard Hough Transform to localise line segments that characterise insert edges. We use this information and the geometry of the insert to identify which of these line segments is the cutting edge. The output of our algorithm is a set of quadrilateral regions around the identified cutting edges. These regions can then be used as input to other algorithms for the quality assessment of the cutting edges. Our results show that the proposed method is very effective for the localisation of the cutting edges of inserts in an edge profile milling machine.

Recognition and retrieval of objects in diverse applications

“…The output of our algorithm is a set of quadrilateral regions around the identified cutting edges that can be used as input to other methods specialised in assessing the state of the cutting edge. Our proposal [4] is very effective (accuracy equals to 99.61%) for the localisation of the cutting edges of inserts in an edge profile milling machine. Following up this result, we studied how to recognise broken inserts because it is critical for a proper tool monitoring system [5].…”

A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques

“…As Fernandez-Robles et al pointed out [25], the resting time of milling head tools lies between 5 and 30 minutes. Our experiments, ran on a computer with an i7 processor and 16GB RAM using MATLAB, showed that it takes about 60 seconds to describe the full insert dataset and less than 0.1 seconds to classify all the training data, so the implementation reaches real time performing on a production environment.…”

Machine-vision-based identification of broken inserts in edge profile milling heads

“…In our application, the head tool contains 30 rhombohedral inserts leading to 8-10 visible inserts per image, which makes the localisation of the inserts a new and challenging task. In our previous work [20], we introduced a method to localise inserts in images of such head inserts and here we improve it and propose a new method that evaluates the status of the inserts.…”