Image Super Resolution by Double Dictionary Learning and its Application to Tool Wear Monitoring in Micro Milling

作     者：Li, Si Lin, Zhihao Zhu, Kunpeng 

作者机构：Precision Manufacturing Institute Wuhan University of Science and Technology Hubei Wuhan430081 China Research Center of Advanced Manufacturing Technology Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Jiangsu Changzhou213164 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan430081 China 

出 版 物：《SSRN》 

年 卷 期：2023年

核心收录：

主　　题：Milling (machining) 

摘      要：It is an effective means to improve the machining quality of product by monitoring the tool wear conditions in micro milling. However, due to the small size and high speed of the micro milling cutter, it is difficult to capture the clear image of tool wear by CCD camera. Therefore, a double dictionary sparse coding super-resolution (SR) method is proposed to improve the resolution of micro milling tool wear images in this work. Based on the traditional single dictionary, this work proposes the residual dictionary to reconstruct the residual image of the tool, achieving the double dictionary coefficient encoding reconstruction. In order to extract local features of low resolution (LR) tool wear images, the Gobor filter banks are designed, and then the PCA method is used to reduce dimension of extracted features. The dictionary learning based on a single high resolution (HR) image is realized, which reduces the dependence of super-resolution algorithm on large datasets. The effectiveness of Gabor filter and double dictionary for restoration tool wear image details is verified by experiments. Compared with other conventional SR methods, the results have shown that the proposed method has excellent performance in super-resolution reconstruction of micro milling tool images for tool wear monitoring. © 2023, The Authors. All rights reserved.