Abstract:

Objective The objective of the research on the non-contact detection system for yarn tension based on machine vision is to improve the accuracy of tension detection for moving yarns. The current contact tension measurement makes direct contact with yarn during the detection process, which causes additional friction to the yarn and wears the measuring device at the same time, affecting the accuracy of tension measurement. The machine vision technology provides a new direction for yarn tension detection. Based on string vibration theory and machine vision image processing method, this poper aimed to study and design a non-contact detection system for yarn tension based on machine vision to achieve accurate tension detection.

Method In order to investigate the relationship between yarn tension and vibration frequency in non-contact detection, the yarn in transverse vibration in the system was regarded as string vibration, and a mathematical model of yarn tension and vibration frequency was established and the theoretical relationship was derived. The yarn winding motion device was designed, the camera shooting field of view was planned, and the yarn tightening roller was designed to make the yarn vibrate freely in the detection range. A line array industrial camera was selected to complete the yarn image acquisition, and the strip light source was adopted to assist the illumination. A series of image pre-processing was carried out to smooth out the yarn image noise, edge extraction was selected to obtain the upper boundary of the image, and the frequency was calculated from the peaks and valleys of two adjacent frames to measure the yarn tension.

Results In order to verify the accuracy of yarn tension measurement, test experiments were conducted using the constructed detection device, where the yarn was wound around the winding roller and placed in the middle of the pulley and clamping roller (Fig.7). A tension sensor was used for tension detection comparison, the yarn guide roller and the winding roller were driven by servo motors, and the line array light source provided stable light. The frame rate of the image acquisition by the line array camera was set to 100 frames per second, and the exposure time was 100 ms. Three strands of Kevlar yarn were used in the experiment, where the yarn length was 50 m, the yarn linear density was 8.2 tex, and the yarn diameter was 0.6 mm. During the experiment, the yarn moved at about 25 m/min, the winding speed of the winding roller motor was 180 r/min, and the winding speed of the clamping roller motor was 98.2 r/min. The fitted line of yarn vibration frequency and tension showed, where it was evident that according to the time sequence, the measured value of the tension sensor correponds to that of the vision measurement system (Fig.9). The results showed that yarn frequency was positively correlated with yarn tension, and the yarn vibration frequency was quadratically related to yarn tension, and the correlation coefficient of the fit reached 0.992. The deviation of the system measured values and the sensor measured values were different, and the collected frequency information effectively reflect the stability of the yarn tension was indicated (Tab.1 and Fig.10). The results showed that the current tension of the moving yarn determined by the vibration frequency is within the tension range of 5-30 cN, and the relative error rate between the visual system measured value and the tension sensor measured value was about ±10%.

Conclusion When the yarn running state changes, the yarn moving speed will also change and so will the measurement value of the vision system. In the vision measurement system, when the yarn runs to the winding roller and clamping roller, the yarn is subjected to a relatively large force. When the yarn runs to the middle, the yarn tends to run smoothly and the tension will be relatively uniform, causing the detected yarn tension to fluctuate within a certain range. The non-contact yarn tension detection does not directly contact the yarn and will not change the original force state of the yarn. Theoretically, the measurement accuracy is higher than the direct contact method, which proves the feasibility of the non-contact detection system to complete the yarn tension measurement and has reference significance for the research of non-contact detection of yarn tension and has a broad prospect in engineering application.

Key words: yarn tension, non-contact detection, string vibration, tension sensor, machine vision