Study Of Acoustic Surface Wave Yarn Tension Sensor

As a pillar industry supporting national economic and social development,the textile industry is integrating with the new generation of information technology,accelerating the pace of digital and intelligent transformation.In modern textile production,the application of various sensors is the primary link to achieve automatic detection and control,an important component of the development of electromechanical integration,and a necessary choice to adapt to the industry development trend.As an important component of intelligent weaving equipment,the application of yarn tension sensors in textile production directly affects production efficiency and textile quality.Among numerous yarn tension sensors,the surface acoustic wave yarn tension sensor integrates acoustic theory,piezoelectric materials,and electronic technology research,with small size,easy integration,high sensitivity,stable and simple structure,fast response time,strong anti-interference ability and low production cost and many other advantages,is a good choice to adapt to the development trend of today’s textile industry.This dissertation focuses on three key issues of the surface acoustic wave yarn tension sensor,which are: the improvement of temperature stability,the optimization of sensitivity and the study of new structural forms.The main contributions are as follows:(1)In this dissertation,two software methods based on data fusion technology,binary regression analysis and least-squares support vector machine algorithm optimized by particle swarm optimization algorithm,are used to solve the temperature cross-sensitivity problem of the acoustic surface wave yarn tension sensor and improve the temperature stability of the sensor,respectively.This temperature compensation method,which is in line with the intelligent development trend of yarn tension sensors,has many advantages such as high accuracy,easy debugging,high flexibility,and wide application.From the research results,these two temperature compensation methods based on data fusion technology reduce the sensitivity temperature coefficient of the sensor from 2.888×10-2/℃ before compensation to 5.285×10-3/℃ and 2.287×10-3/℃ after compensation,respectively;the temperature additional error is reduced from 45.62% before compensation to 8.35% and 3.61% after compensation,respectively.(2)In this dissertation,a sensitivity analysis method of the acoustic surface wave yarn tension sensor based on elasticity theory is proposed.By this theoretical analysis method,the sensitivity optimization problem of the acoustic surface wave yarn tension sensor can be transformed into the solution problem of the bending moment distribution of the piezoelectric substrate.This provides a simple and effective theoretical analysis tool for the dimensional design and structural design of the acoustic surface wave yarn tension sensor.(3)This dissertation designs and tests a simple supported beam acoustic surface wave yarn tension sensor.The sensitivity test shows that the sensitivity of the simple supported beam acoustic surface wave yarn tension sensor is improved by 2.5 times compared with that of the fixed beam sensor.(4)In this dissertation,two types of(different distances between supports)single-end overhanging beam acoustic surface wave yarn tension sensors were designed and tested with 2.0 times and 7.4 times of the sensitivity of the fixed beam sensor,respectively.It is shown that the sensitivity of the sensor can be increased by reducing the distance between the two supports of the piezoelectric substrate without changing the size of the piezoelectric substrate.This provides a design idea for achieving adjustable sensitivity for this type of sensor.In addition,the study of new structural forms provides more research lines for the optimization goal of the acoustic surface wave yarn tension sensor to obtain greater sensitivity with smaller dimensions,and also helps to improve the general applicability of the sensor.