Dynamic Perception and Experimental Study of Tactile Texture Based on Ultrasonic Resonance Squeeze Film Effect

doi:10.3969/j.issn.1004-132X.2025.11.012

Abstract

Abstract:

Using the mechanism that modulating sliding friction at the contact interfaces might perceive changing tactile texture features， an ultrasonic resonance tactile dynamic perception device was proposed based on the squeeze film effect. The theoretical model of the friction-reducing mechanism of the squeeze film effect was constructed under the high squeeze factor of the squeeze film， and the mapping relationship between the friction-regulating performance and the actual material texture was established through the friction factor calibration experiments. The results show that the device may achieve the dynamic regulation of surface friction factor is as 0.295~0.807 under the excitation of 36.314 kHz resonance signals and the voltage amplitude is as 0~200 V. A LSTM neural network was used to construct a temporal force signal and tactile texture prediction model to objectively evaluate the tactile texture reproduction performance of the device， and the average error of the model prediction results is as 3.33%， which verifies the device has a good reproduction effectiveness of tactile texture.

Key words: ultrasonic vibration, squeeze film effect, friction modulation, haptic reproduction, long short-term memory（LSTM） neural network

摘要：

利用调控接触界面的滑动摩擦可以感知变化的触觉纹理特征的机制，基于挤压膜效应提出了一种超声谐振触觉动态感知装置。在挤压膜的高挤压状态下，构建挤压膜效应减摩机制理论模型，通过摩擦因数标定实验建立摩擦调控性能与实际材质纹理的映射关系。研究结果表明，该装置在频率为36.314 kHz的谐振信号激励下，电压幅值在0~200 V时可实现表面摩擦因数0.295~0.807的动态调控。利用长短期记忆（LSTM）神经网络构建时序力信号与触觉纹理预测模型，客观评估装置的触觉纹理再现性能，得到该模型预测结果的平均误差为3.33%，验证了装置具有较好的触觉纹理再现效果。

关键词: 超声振动, 挤压膜效应, 摩擦调控, 触觉再现, 长短期记忆（LSTM）神经网络

CLC Number:

TH113.1

Zhibo CHEN, Guoping LI, Sitong XIANG, Yanding WEI. Dynamic Perception and Experimental Study of Tactile Texture Based on Ultrasonic Resonance Squeeze Film Effect[J]. China Mechanical Engineering, 2025, 36(11): 2574-2582.

陈智博, 李国平, 项四通, 魏燕定. 基于超声谐振挤压膜效应的触觉纹理动态感知及实验研究[J]. 中国机械工程, 2025, 36(11): 2574-2582.

Figures/Tables 19

Fig.1 Schematic diagram of device structure

Fig.2 Vibration modes of piezoelectric vibrators

Tab.1 Main dimensional parameters of the device

参数	数值
压电振子半径/mm	12.5
压电陶瓷层厚度/mm	2
玻璃覆层厚度/mm	2
背衬架支撑部壁厚/mm	1

Fig.3 Finite element analysis results

Fig.4 Variation of friction factor with excitation frequency and voltage amplitude

Fig.5 Resonant frequency measurement system

Fig.6 Vibration response under swept signal excitation

Fig.7 Vibration amplitude as a function of excitation voltage

Fig.8 Experimental setup for calibration of friction factor

Fig.9 Friction factor calibration experimental platform

Fig.10 Initial friction factor calibration

Fig.11 Results of friction modulation experiments

Fig.12 Nonlinear fitting relationship for friction factor variation with excitation voltage

Fig.13 Friction factors and confidence intervals for six material surfaces

Tab.2 Control signal parameters for six material textures

材质纹理	激励频率/kHz	激励电压/V
打印用纸	36.314	107
磨砂玻璃		75
塑料（ABS）		129
碳纤维		179
木纹（顺纹）		98
木纹（逆纹）		84

Fig.14 Schematic diagram of the LSTM neural network structure

Fig.15 Model prediction results

Fig.16 Force signal curve

Tab.3 Prediction results of the temporal force signal and tactile texture prediction model

材质纹理	设定纹理值	预测纹理值	误差/%
打印用纸	1	1.0621	6.21
磨砂玻璃	2	1.9929	0.71
塑料（ABS）	3	2.9434	5.66
碳纤维	4	3.9939	0.61
木纹（顺纹）	5	4.9951	0.49
木纹（逆纹）	6	5.9368	6.32

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