Research on Cantilever Beam Roller Tension Sensor Based on Surface Acoustic Wave

Area of Science:

• Mechanical Engineering
• Sensor Technology
• Signal Processing

Background:

• Surface Acoustic Wave (SAW) sensors are susceptible to temperature variations, affecting tension measurement accuracy.
• Existing tension detection methods may lack sensitivity or require complex calibration.
• Continuous monitoring of tension is crucial in various industrial applications, such as textile manufacturing.

Purpose of the Study:

• To design a highly sensitive continuous tension detection sensor using a cantilever beam structure.
• To develop a robust temperature compensation method for SAW tension sensors.
• To improve the overall accuracy and reliability of tension measurement systems.

Main Methods:

• A novel cantilever beam roller structure was designed to amplify silk thread tension signals.
• COMSOL finite element method (FEM) simulations were used to optimize Interdigital Transducer (IDT) placement for enhanced SAW sensor sensitivity.
• An unbalanced split IDT design was implemented to mitigate parasitic responses.
• A Genetic Algorithm-Particle Swarm Optimization-Backpropagation (GA-PSO-BP) neural network was developed for temperature drift compensation.

Main Results:

• The GA-PSO-BP algorithm significantly reduced the temperature sensitivity coefficient by an order of magnitude compared to raw data.
• The optimized data showed a reduction of 6.0409×10-3 °C-1 and 3.0312×10-3 °C-1 compared to BP and PSO-BP algorithms, respectively.
• The average output error was reduced by 5.748% after compensation, outperforming BP and PSO-BP algorithms.

Conclusions:

• The proposed cantilever beam structure enhances tension transmission sensitivity to SAW sensors.
• The GA-PSO-BP algorithm provides effective temperature compensation for SAW tension sensors.
• This research offers innovative design strategies for advanced tension sensing applications.