A vision-based machine accuracy measurement method

Irino, Naruhiro; Shimoike, M.; Mori, Kotaro; Yamaji, Iwao; Mori, Masahiko

doi:10.1016/j.cirp.2020.04.107

Cited by 18 publications

(5 citation statements)

References 9 publications

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“…Sci. 2024, 14, x FOR PEER REVIEW 2 of 22 making it well suited for online measurement and sorting [9,10]. In recent years, some scholars have applied sub-pixel detection algorithms to gear measurement, resulting in improved measurement accuracy.…”

Section: Gear Integrated Error Curvementioning

confidence: 99%

“…Unlike previous gear contact measurement technologies, gear vision measurement technology belongs to non-contact measurement with high measurement efficiency, making it well suited for online measurement and sorting [9,10]. In recent years, some scholars have applied sub-pixel detection algorithms to gear measurement, resulting in improved measurement accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method

Fang,

Shi,

Sun

et al. 2024

Applied Sciences

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A gear integrated error, a combination of individual and composite errors, carries richer information and has long been a key target of classic gear error measurement techniques. However, in the age of intelligent manufacturing, the classic methods for gear integrated error measurement are no longer able to meet the emerging requirements of large-scale gears and real-time online measurement. To address this gap, a novel approach to obtaining the gear integrated error based on GTC−Facet (Gaussian template convolution-Facet) is proposed. This method accurately pinpoints the sub-pixel contour of gears in images, enabling a quick derivation of the gear integrated error curve. From this curve, other individual and composite errors can be analyzed. The gear error information obtained through our method has higher measurement accuracy, achieving a positioning accuracy of 3.6 μm for the gear profile. Moreover, during the measurement process, the measured gear remains unclamped, and the entire measurement process can be completed within 0.35 s, which is much faster than classic methods. Our method meets the demands of online measurements and provides a new avenue for gear error measurement.

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Section: Gear Integrated Error Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method

Fang,

Shi,

Sun

et al. 2024

Applied Sciences

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“…In practice, it has a huge impact on the detection of product merit and damage to parts. Generally, through image pre-processing, the noise or redundant information in the original image is removed (Irino et al, 2020) • The main novelties/contributions of the proposed method are stated as follows:…”

Section: Introductionmentioning

confidence: 99%

Machine visual technology based steel corrosion evaluation and analysis of power transformation lines: A Zhejiang power grid case study

Liu

Zhang

et al. 2023

Front. Energy Res.

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It can be seen that the corrosion failure of transmission and transformation equipment has increasingly restricted the safe operation of the Zhejiang power grid bottleneck problem. If effective anticorrosion measures are not taken promptly, transmission and transformation equipment serving in various sophisticated atmospheric environments will suffer serious corrosion damage in a relatively short period, which endangers the safe usage of transmission and transformation equipment and the security of grid operation. In this article, through the establishment of transmission and transformation steel components corrosion fracture mechanics model, a standard corrosion spectrum grading software based on DeepLabV3+ image segmentation technology is developed to determine the quantitative assessment method of corrosion damage and assess the corrosion status with safety degree of transmission and transformation equipment. According to the assessment results, the operation and maintenance units are guided to adopt differentiated corrosion maintenance and replacement strategies, so as to reduce corrosion safety hazards and reduce safety accidents and economic losses caused by corrosion, which is of great significance for the safe operation of power grids.

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“…The experimental results show that the machine vision system is capable of measuring the gear parameters to determine the acceptance and rejection of the gear. Irino et al [22] used a machine vision-based method for the accurate measurement of the machine tools, where the images were captured by using low-cost cameras. This method was found to be capable of measuring the entire working space of the machine tool with the greatest accuracy within a short period of time.…”

Section: Introductionmentioning

confidence: 99%

A novel IoT based machine vision system for on-machine diameter measurement and optimization

Zende,

Pawade

2023

Eng. Res. Express

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The use of machine vision systems has been made user-friendly, cost-effective, and flawless by the rapid development in the fields of advanced electro-optical and camera systems, electronics systems, and software systems. One such application of machine vision systems in the field of manufacturing is the inspection of a semi-finished or finished component during an ongoing manufacturing process. In this study, the camera's intrinsic and extrinsic parameters were maintained constant, while red, green, and blue light sources were employed to measure the component diameter using pixel analysis. A novel approach was used in an IoT-based machine vision system where, on the same image, the smartphone camera was calibrated and the image diameter of the component under study was measured, which was found to be quite accurate. Four different cases were used in the error analysis of image diameter, in which experimental results show that under blue light, the percentage pixel error span is the largest at 0.2624 % followed by 0.1422 % under green light and 0.0903 % under red light. Further, the use of four different cases was followed by the 'Weighted Sum Model', which optimized the percentage errors in estimated actual diameter precisely and effectively, where outcome results showed that the approximate percentage errors were determined within 0.8 % for blue light, 0.5 % for a red light, and 0.1 % for a green light. The proposed IoT-based machine vision system was found to be robust and effective for on-machine measurement.

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A vision-based machine accuracy measurement method

Cited by 18 publications

References 9 publications

Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method

Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method

Machine visual technology based steel corrosion evaluation and analysis of power transformation lines: A Zhejiang power grid case study

A novel IoT based machine vision system for on-machine diameter measurement and optimization

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