# Cutting Blade Measurement of an Ultrasonic Cutting Machine Using Multi-Step Detection
## 1 Introduction  
With the development of ultrasonic cutting technology, an ultrasonic vibration cutting machine is used for the precise cutting of fabrics, rubber, thermoplastic foil, non-woven fabric, etc. In particular, cutting machines using ultrasonic vibration are capable of cutting very precisely without exerting any pressure on the objects in order to avoid any surface deformation. Ultrasonic vibration cutting also features very high cutting speeds and reduced cleaning costs, which has increased their demand for use in automation machines [1]. However, when the ultrasonic vibration cutting machine is used in connection with an automatic machine (such as robot arm), it is difficult to recognize the replacement cycle or the degree of wear and tear of the cutting blade [2]. This process results in inaccurate cutting of the object and material damage. For this reason, the development of an automation inspection system, which is based upon the machine vision system, is actively underway [3; 4]. The machine vision system acquires images using a camera and then quantitatively judges [5] the degree of abrasion and warping/breakage of the cutting blade. In order to improve the productivity and reliability, machine vision inspection equipment is commonly used in actual practice. Recently, the machine vision has been actively deployed in research for the development of measurement systems for different applications. Measurement of the dimensions of an O-ring [5], detecting defects in a thin film transistor liquid crystal display (TFT-LCD) [6], measurement of the refractory bricks [7], and measuring the size of an automobile break pad [8] are the few areas benefited by the machine vision. In general, various methods, like brightness information, shape information, pattern information, etc., are used for object recognition in the field of image processing. Among these methods of object detection, template matching and feature point detection are the techniques which have gained more attraction [5]. However, template matching detects an object by determining the similarity of the image with a reference image that is not adaptive to the change of the reference image, which consequently deteriorates the results. On the other hand, the feature extraction method uses the shape and color information of the object or the blob information of the object based on the binarization. The detection method using the shape and color information of the object is sensitive to the change of color depending on the illumination. If the shape of the object is not specified, it is difficult to define the feature of the shape. In the object detection method based on blob detection, it is difficult to find the target object when there are objects other than the object to be detected [9; 10]. In this paper, we propose a machine vision-based image processing algorithm that measures the thickness and length of the cutting blade to detect its bending and fracture, and also measures the rotation angle of the cutting blade in order to check the operation of the robot-arm. The region of interest (ROI) is extracted from the cutting blade area of the original image. The binarization based on the curvature of the extracted image is performed by the proposed method. The background region is extracted and removed from the binarization image, which is followed by the removal of various noises included in the extracted image. In order to recover the information of the cutting blade's tip, which was lost in the fine noise removal process, only the tip of the cutting blade is extracted again from the background-removed image by assigning a new ROI only for the tip of the cutting blade. The thickness, length, and rotation angle of the cutting blade are then measured for the restored image. This paper is divided into two main sections.