Visual Inspection for Breakage of Micro-milling Cutter

¹ School of Physics and Mechanical & Electrical Engineering, Xiamen University, Xiamen, 361005, China
² Xiamen Tungsten Co., Ltd., Xiamen, 361005, China
³ School of Information Science and Technology, Xiamen University, Xiamen, 361005, China
³ Tel.: +86 592 2580135

³ E-mail: heshan@xmu.edu.cn

Received: 23 July 2014 /Accepted: 30 October 2014 /Published: 30 November 2014

Abstract: In order to realize visual inspection for breakage of micro-milling cutter, a developed image acquisition method of the surface of a micro-milling cutter was constructed and a classification method based on multilayer neural network was proposed in this article. While the milling cutter was rotating at a constant speed, a camera was triggered by a rotary encoder to capture a series of images. And the developed image of milling cutter was created by image mosaic algorithms. The moment of regional feature as well as the gray feature of the tooth edge was extracted as the input vector of neural network. The feature vector includes moment of inertia, geometric central moment, three-dimensional invariants moment and the gray value of the projection on two principal axis directions of the tooth region. By designing a proper neural network, breakage defects can be detected 100 %. And the false discovery rate is 0.5 %.

Keywords: Visual inspection, Micro-milling cutter, Breakage defects, Pattern recognition.

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