用异常模型对导轨块进行分类

IF 3.1 4区 医学 Q2 BIOPHYSICS Journal of Applied Biomaterials & Functional Materials Pub Date : 2022-12-30 DOI:10.35745/afm2022v02.04.0003
Jun-Jie Liao, Jing-Wei Zhang, Bing-En Liu, K. Lee
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引用次数: 1

摘要

直线导轨块是用于直线滑动导轨上刮去导轨上油污的附件,安装在滑块的前后端。它们还用于铣床、车床、自动化机械、机械臂、电子仪器等。目前行业对这种钢轨块的质量检测主要依靠人力进行,难以标准化。因此,引入了自动化和数字化深度学习检测技术进行检测。为了了解深度学习技术应用于线性导块检测过程的适用性,我们采用卷积神经网络模型架构并使用Xception模型。在模型训练中,通过放大图像的方法和检测多种缺陷来提高训练效果。通过异常模型,30次后的训练准确率约为98.7%,验证准确率约为97.4%,测试准确率约为91.8%。
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Classification of Guide Rail Block by Xception Model
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%.
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来源期刊
Journal of Applied Biomaterials & Functional Materials
Journal of Applied Biomaterials & Functional Materials BIOPHYSICS-ENGINEERING, BIOMEDICAL
CiteScore
4.40
自引率
4.00%
发文量
36
审稿时长
>12 weeks
期刊介绍: The Journal of Applied Biomaterials & Functional Materials (JABFM) is an open access, peer-reviewed, international journal considering the publication of original contributions, reviews and editorials dealing with clinical and laboratory investigations in the fast growing field of biomaterial sciences and functional materials. The areas covered by the journal will include: • Biomaterials / Materials for biomedical applications • Functional materials • Hybrid and composite materials • Soft materials • Hydrogels • Nanomaterials • Gene delivery • Nonodevices • Metamaterials • Active coatings • Surface functionalization • Tissue engineering • Cell delivery/cell encapsulation systems • 3D printing materials • Material characterization • Biomechanics
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