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Machine Vision Online Detection of Ore Granularity Based on Edge Computing

Jiang Yao Yinbo Xue Xiaoliang Li Lei Zhai Zhenyu Yang Wenhui Zhang
Archives of Mining Sciences | 2023 | vol. 68 | No 2 | 335-350 | DOI: 10.24425/ams.2023.146183
Keywords Ore granularity machine vision online detection Edge computing
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Abstract

Belts are widely applied in mine production for conveying ores. Understanding ore granularity, which is a crucial factor in determining the effectiveness of crushers, is vital for optimising production efficiency throughout the crushing process and ensuring the success of subsequent operations. Based on edge computing technology, an online detection method is investigated to rapidly and accurately obtain ore granularity information on high-speed conveyor belts. The detection system utilising machine vision technology is designed in this paper. The high-speed camera set above the belt is used to collect the image of the ore flow, and the collected image is input into the edge computing device. After binary, grey morphology and convex hull algorithm processing, the particle size distribution of ore is obtained by statistical analysis. Finally, a 5G router is used to output the settlement result to a cloud platform. In the GUANBAOSHAN mine of Ansteel Group, the deviation between manual screening and image particle size analysis was studied. Experimental results show that the proposed method can detect the ore granularity, ore flow width and ore flow terminal in real-time. It can provide a reference for the staff to adjust the parameters of the crushing equipment, reduce the mechanical loss and the energy consumption of the equipment, improve the efficiency of crushing operation and reduce the failure rate of the crusher.
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Authors and Affiliations

Jiang Yao
1
Yinbo Xue
2
Xiaoliang Li
2
Lei Zhai
2
Zhenyu Yang
3
Wenhui Zhang
3
  1. Northeastern University, China
  2. Chinese Academy of Sciences Allwin Technology Co., Ltd, China
  3. Ansteel Group Guanbaoshan Mining Co., Ltd, China

Enhanced of Tool Management Based on Machine Vision in the Field of Metal Forming Technology

S. Świłło R. Cacko
Archives of Metallurgy and Materials | 2022 | vol. 67 | No 4 | 1419-1425 | DOI: 10.24425/amm.2022.141069
Keywords die aluminium extrusion tool management machine vision digital image processing
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Abstract

In this article, the authors focused on the widely used aluminium extrusion technology, where the die quality and durability are the essential factors. In this study, detailed solutions in the three-key area have been presented. First is applying marking technology, where a laser technique was proposed as a consistent light source of high power in a selected, narrow spectral range. In the second, an automated and reliable identification method of alphanumeric characters was investigated using an advanced machine vision system and digital image processing adopted to the industrial conditions. Third, a proposed concept of online tool management was introduced as an efficient process for properly planning the production process, cost estimation and risk assessment. In this research, the authors pay attention to the designed vision system’s speed, reliability, and mobility. This leads to the practical, industrial application of the proposed solutions, where the influence of external factors is not negligible.
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Authors and Affiliations

S. Świłło
1
ORCID: ORCID
R. Cacko
1
ORCID: ORCID
  1. Warsaw University of Technology, Metal Forming and Foundry, Faculty of Mechanical and Industrial Engineering, 85 Narbutta Str., 02-525, Warszawa, Poland

Automatic Calibration System for Digital-Display Vibrometers Based on Machine Vision

Wen He Guanhua Xu Zuochao Rong Gen Li Min Liu
Metrology and Measurement Systems | 2014 | No 2 | 317-328 | DOI: 10.2478/mms-2014-0027
Keywords Image recognition calibration vibration measurement machine vision automatic testing digitaldisplay vibrometer
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Abstract

Considering the low efficiency during the process of traditional calibration for digital-display vibrometers, an automatic calibration system for vibrometers based on machine vision is developed. First, an automatic vibration control system is established on the basis of a personal computer, and the output of a vibration exciter on which a digital-display vibrometer to be calibrated is installed, is automatically adjusted to vibrate at a preset vibration level and a preset frequency. Then the display of the vibrometer is captured by a digital camera and identified by means of image recognition. According to the vibration level of the exciter measured by a laser interferometer and the recognized display of the vibrometer, the properties of the vibrometer are calculated and output by the computer. Image recognition algorithms for the display of the vibrometer with a high recognition rate are presented, and the recognition for vibrating digits and alternating digits is especially analyzed in detail. Experimental results on the built-up system show that the prposed image recognition methods are very effective and the system could liberate operators from boring and intense calibration work for digital-display vibrometers

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Authors and Affiliations

Wen He
Guanhua Xu
Zuochao Rong
Gen Li
Min Liu

A novel method for 3D measurement of RFID multi-tag network using a machine vision system

Zhuang Xiao Xiaolei Yu Zhimin Zhao Wenjie Zhang Zhenlu Liu Dongsheng Lu Dingbang Dong
Metrology and Measurement Systems | 2018 | vol. 25 | No 3 | 475-486 | DOI: 10.24425/123898
Keywords 3D measurement RFID multi-tag network dual-CCD system neural network machine vision
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Abstract

The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag

networks is one of the important issues in the field of RFID, which affects the reading performance of

RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag

networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of

RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological

filtering method are used to process the images. The template matching method is respectively used to

determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the

3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model

the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance.

The BP neural network can predict the reading distances of unknown tag groups and find out the optimal

distribution structure of the tag groups corresponding to the maximum reading distance. In the future work,

the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.

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Authors and Affiliations

Zhuang Xiao
Xiaolei Yu
Zhimin Zhao
Wenjie Zhang
Zhenlu Liu
Dongsheng Lu
Dingbang Dong

Lane Detection by Dynamic Origin Technique for Advanced Driver Assistance System

P. Maya C. Tharini
International Journal of Electronics and Telecommunications | 2021 | vol. 67 | No 4 | 589-594 | DOI: 10.24425/ijet.2021.137850
Keywords Advanced Driver Assistance System (ADAS) Machine Vision Research Lane Detection Piecewise Linear Stretching Function Slope Detection
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Abstract

Lane detection is one of the key steps for developing driver assistance and vehicle automation features. A number of techniques are available for lane detection as part of computer vision tools to perform lane detection with different levels of accuracies. In this paper a unique method has been proposed for lane detection based on dynamic origin (DOT). This method provides better flexibility to adjust the outcome as per the specific needs of the intended application compared to other techniques. As the method offers better degree of control during the lane detection process, it can be adapted to detect lanes in varied situations like poor lighting or low quality road markings. Moreover, the Piecewise Linear Stretching Function (PLSF) has also been incorporated into the proposed method to improve the contrast of the input image source. Adding the PLSF method to the proposed lane detection technique, has significantly improved the accuracy of lane detection when compared to hough transform method from 87.88% to 98.25% in day light situations and from 94.15% to 97% in low light situations.
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Bibliography

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Authors and Affiliations

P. Maya
1
C. Tharini
2
  1. B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
  2. B S Abdur Rahman Crescent Institute of Science and Technology,Chennai, India

New approach to spindle thermal extension measuring based on machine vision for the vertical maching centre

Dongxu Su Xin Cai Yang Li Wanhuan Zhao Huijie Zhang
Metrology and Measurement Systems | 2021 | vol. 28 | No 2 | 357-370 | DOI: 10.24425/mms.2021.136612
Keywords Spindle thermal extension measuring machine vision Gaussian sub-pixel fitting thermal error compensation
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Abstract

When machine tool spindles are running at a high rotation speed, thermal deformation will be introduced due to the generation of large amounts of heat, and machining accuracy will be influenced as a result, which is a generalized issue in numerous industries. In this paper, a new approach based on machine vision is presented for measurements of spindle thermal error. The measuring system is composed of a Complementary Metal-Oxide-Semiconductor (CMOS) camera, a backlight source and a PC. Images are captured at different rotation angles during end milling process. Meanwhile, the Canny edge detection and Gaussian sub-pixel fitting methods are applied to obtain the bottom edge of the end mill which is then used to calculate the lowest point coordinate of the tool. Finally, thermal extension of the spindle is obtained according to the change of the lowest point at different time steps of the machining process. This method is validated through comparison with experimental results from capacitive displacement sensors. Moreover, spindle thermal extension during the processing can be precisely measured and used for compensation in order to improve machining accuracy through the proposed method.
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Authors and Affiliations

Dongxu Su
1
Xin Cai
1
Yang Li
1
ORCID: ORCID
Wanhuan Zhao
1
Huijie Zhang
1
  1. Xi’an Jiaotong University, State Key Laboratory for Manufacturing System Engineering, Xi’an, Shaanxi 710054, China

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