Computer vision for yarn microtension measurement

Wang, Qing; Lu, Changhou; Huang, Ran; Pan, Wei; Li, Xueyong

doi:10.1364/ao.55.002393

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…By applying the parameters in Table 1 to Equation (11), the first natural frequency of PFY can be calculated by using the Runge-Kutta method and fast Fourier transform (FFT). Using Equation (15), the first natural frequency from the axially moving string model can also be obtained.…”

Section: Comparison Analysis Of Two Modelsmentioning

confidence: 99%

“…Wang et al. 11 proposed an optical method to measure the tension of moving yarn in the winding system based on charge-coupled device (CCD) line scan technology. However, the analysis of images requires a precise model and a huge amount of computation, which is difficult to achieve for PFY tension detection under high-speed spinning conditions.…”

mentioning

confidence: 99%

“…The balloon tension was calculated by comparing the yarn strain (occurring in the balloon zone) measured by a digital image analysis program with the stress-strain curve of the yarn produced. Wang et al 11 proposed an optical method to measure the tension of moving yarn in the winding system based on charge-coupled device (CCD) line scan technology. However, the analysis of images requires a precise model and a huge amount of computation, which is difficult to achieve for PFY tension detection under high-speed spinning conditions.…”

mentioning

confidence: 99%

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Non-contact detection of polyester filament yarn tension in the spinning process by the laser Doppler vibrometer method

Zhang

Tan

et al. 2021

Textile Research Journal

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The precise detection of polyester filament yarn (PFY) tension in the spinning process is critical to ensure product quality. The laser Doppler vibrometer (LDV) method is proposed to achieve non-contact detection of PFY tension in this paper. By employing the Hamilton principle, the transverse dynamics differential equations of PFY are derived, which are discretized and solved by the Galerkin method and Runge–Kutta method, respectively. In the equations, the PFY between two adjacent rollers is simplified as an axially moving string to verify the generality of calculating natural frequencies. The calculated natural frequencies from the axially moving string model are compared with solved results from the transverse dynamics differential equations. It is shown that the approximation of natural frequencies can be obtained from the axially moving string model. This study attempts to establish an approximate generic model among the PFY tension, the spinning speed and the first natural frequency based on axially moving string model, from which the PFY tension can be calculated efficiently by employing the measured natural frequencies. The LDV method is used to measure the natural frequencies. A major advantage of the proposed method is to realize non-contact detection of PFY tension. The method is more useful under high-speed spinning conditions where contact tension detectors are not available. An experimental analysis is carried out to verify the effectiveness and accuracy of the proposed method. Therefore, it is believed that the non-contact detection of PFY tension in the spinning process by the LDV method is feasible.

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Section: Comparison Analysis Of Two Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Non-contact detection of polyester filament yarn tension in the spinning process by the laser Doppler vibrometer method

Zhang

Tan

et al. 2021

Textile Research Journal

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“…In recent years, line scan cameras have been increasingly widely used due to their simplicity and high precision [9]. In this paper, a method based on line scan vision is presented for the dynamic recognition of the readout of pointer meters.…”

Section: Introductionmentioning

confidence: 99%

Automatic pointer meters recognition system based on line scan vision

Wang¹

2022

Meas. Sci. Technol.

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The automatic recognition of pointers is of great significance for efficiently collecting measurements from industrial instruments. In this paper, an automatic pointer meter recognition system based on line scan vision is developed. A line scan camera is used to capture images of the pointer of a pointer meter. Light-spot centroid algorithm is implemented to determine the centroid position of the pointer. The captured images can record the dynamic movement of the pointer, thereby enabling condition monitoring in industrial processes. Experimental results show that the proposed pointer meter extraction method is robust against interference and that a characteristic segmentation classifier produces more accurate detection results than other approaches.

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“…This will result in lower quality of yarn and less production. 16,17 Yarn tension is affected by so many elements that it is difficult to measure it. As a result, the accurate measurement of yarn tension is an urgent problem.…”

Section: Introductionmentioning

confidence: 99%

Effect of IDT position parameters on SAW yarn tension sensor sensitivity

Lei

Mian

et al. 2020

Measurement and Control

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In this paper, the effect of the interdigital transducer (IDT) position parameters on the surface acoustic wave (SAW) yarn tension sensor sensitivity is investigated. The stress–strain characteristic of substrate was studied by the combination of finite element simulation and regression analysis method. According to this characteristic, the function relationship between the SAW yarn tension sensor sensitivity and the IDT position parameters was built using the regression analysis method. The monotonicity of the regression function was also given. On this basis, a novel sensitivity optimal scheme was proposed and solved by the quadratic programming method. Its solution demonstrates that the optimum sensitivity can be obtained when the IDT is 8.9 mm to the left side of the substrate and the IDT is 0.3 mm to the top edge of the substrate within a domain of the IDT position parameters. The SAW yarn tension sensor with corresponding IDT position parameters was fabricated to validate the correctness of the sensitivity optimal scheme. The measured results indicate that the SAW yarn tension sensor sensitivity can reach 813.69 Hz/g, which confirms that the novel scheme is effective.

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Computer vision for yarn microtension measurement

Cited by 11 publications

References 16 publications

Non-contact detection of polyester filament yarn tension in the spinning process by the laser Doppler vibrometer method

Non-contact detection of polyester filament yarn tension in the spinning process by the laser Doppler vibrometer method

Automatic pointer meters recognition system based on line scan vision

Effect of IDT position parameters on SAW yarn tension sensor sensitivity

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