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Applied Functional Materials
ISSN:2737-5323
Frequency: Quarterly Published by lIKll


Open Access Research Paper
 AFM 2022/12
Vol.2, Iss.4 : 17-24
https://doi.org/10.35745/afm2022v02.04.0003

Classification of Guide Rail Block by Xception Model


Jun-Jie Liao1, Jing-Wei Zhang1, Bing-En Liu1 and Kuang-Chyi Lee1*

1Department of Automation Engineering, National Formosa University, Yunlin 632, Taiwan

Received:November 18, 2022; Revised:December 02, 2022; Accepted:December 18, 2022; Published:December 30, 2022
Abstract:
Linear guide rail blocks are used in linear slide rail accessories to scrape off oil stains in the rails, installed on the front and rear ends of the slider. They are also used in milling machines, lathes, automated machines, robotic arms, electronic instruments, and so on. At present, the industry relies on manpower to carry out the quality inspection of this rail block which is difficult to standardize. Thus, automatic and digital deep learning inspection technology is introduced for the inspection. To understand the suitability of deep learning techniques applied to the linear guide block inspection process, we adopt the convolutional neural network model architecture and use the Xception model. In model training, the training effect is improved by amplifying the image method and testing many different defects. Through the Xception model, the training accuracy is about 98.7% after 30 epochs, the validation accuracy is about 97.4%, and the test accuracy is about 91.8%.

Keywords:  Deep learning, Guide rail block, Classification, Xception

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*Corresponding author; e-mail: kclee@gs.nfu.edu.tw


Citation:Liao, J.J.; Zhang, J.W.; Liu, B.E.; Lee, K.C.Classification of Guide Rail Block by Xception Model. Applied Functional Materials 2022, 2, 17-24. https://doi.org/10.35745/afm2022v02.04.0003

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Copyright: © 2022  The Author(s). Published with license by IIKII, Singapore. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

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