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Abstract

Current vision-based roughness measurement methods are classified into two main types: index design and deep learning. Among them, the computation procedure for constructing a roughness correlation index based on image data is relatively difficult, and the imaging environment criteria are stringent and not universally applicable. The roughness measurement method based on deep learning takes a long time to train the model, which is not conducive to achieving rapid online roughness measurement. To tackle with the problems mentioned above, a visual measurement method for surface roughness of milling workpieces based on broad learning system was proposed in this paper. The process began by capturing photos of the milling workpiece using a CCD camera in a normal lighting setting. Then, the train set was augmented with additional data to lower the quantity of data required by the model. Finally, the broad learning system was utilized to achieve the classification prediction of roughness. The experimental results showed that the roughness measurement method in this paper not only had a training speed incomparable to deep learning models, but also could automatically extract features and exhibited high recognition accuracy.
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Authors and Affiliations

Runji Fang
1
Huaian Yi
1
Shuai Wang
1
Yilun Niu
1

  1. School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China
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Abstract

Workpiece surface roughness measurement based on traditional machine vision technology faces numerous problems such as complex index design, poor robustness of the lighting environment, and slow detection speed, which make it unsuitable for industrial production. To address these problems, this paper proposes an improved YOLOv5 method for milling surface roughness detection. This method can automatically extract image features and possesses higher robustness in lighting environments and faster detection speed. We have effectively improved the detection accuracy of the model for workpieces located at different positions by introducing Coordinate Attention (CA). The experimental results demonstrate that this study’s improved model achieves accurate surface roughness detection for moving workpieces in an environment with light intensity ranging from 592 to 1060 lux. The average precision of the model on the test set reaches 97.3%, and the detection speed reaches 36 frames per second.
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Authors and Affiliations

Xiao Lv
1
Huaian Yi
1
Runji Fang
1
Shuhua Ai
1
Enhui Lu
2

  1. School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006,People’s Republic of China
  2. School of Mechanical Engineering, Yangzhou University, Yangzhou, 225009, People’s Republic of China
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Abstract

To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes.
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Authors and Affiliations

Lingli Lu
1
Huaian Yi
1
Aihua Shu
1
Jianhua Qin
1
Enhui Lu
2

  1. School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China
  2. School of Mechanical Engineering, Yangzhou University, Yangzhou, 225009, People’s Republic of China

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