Properties of Tactile Sensors Based on Resistive Ink and the Dimension of Electrodes

This chapter presents the ongoing research, which aims to select suitable electrodes for their use in the pressure distribution measurement system Plantograf. In our research, we examine more materials, especially Yokohama conductive rubber CS57-7RSC and also conductive inks, which are represented type DZT-3 K, Graphit 33 and mixture of Loctite-Henkel conductive inks type Loctite 7004Hr and Loctite NCI 7002EC. All materials can be used as a converter between pressure and electrical quantities in the design of planar pressure transduces. We build on our previous works, where were examined the properties of conductive rubber, conductive inks and electrodes. Next part is focused on the newest results of our research. Due to the still incomplete results in the given issue, we decided to perform an extensive and original measurement of a total of 172 combinations of different electrode sizes, the ratio of conductive ink mixtures and the thickness of the applied ink layer. Thanks to this, it will be possible in the future to select a suitable combination of electrodes and inks when designing tactile pressure sensors for industrial or medical applications without the need to perform time-consuming preparatory measurements and exclude unsuitable ink-electrode combinations.

Development of a Pressure-Sensitive Conductive Rubber Sensor for Analyzing Meniscal Injury in Porcine Models

The assessment of the distribution of contact pressure on the meniscus is important in the elucidation of kinematics, etiology of joint diseases, and establishment of treatment methods. Compared with sensors widely used in recent years, pressure-sensitive conductive rubber sensors are easy to mold, flexible, durable, and resistant to shearing forces. This study is aimed at developing a rubber sensor for meniscal research and evaluating the pressure distribution after meniscal injury using porcine models. After confirming the reliability of the rubber sensor, contact pressure was obtained from the rubber sensor using the medial meniscus and femur of the porcine knee. Three test conditions of intact meniscus, radial tear, and meniscectomy were prepared, and a compressive load of 100 N was applied. After confirming the high reliability of the rubber sensor, the intact meniscus had the most uniform pressure distribution map, while the pressure in the meniscectomy model was concentrated in the resection region. The high-pressure region was significantly smaller in the intact group than in the radial tear models after 80 and 100 N ( P < 0.05 ). The rubber sensor captured the pressure concentration specific to each examination group and was useful for evaluating the relationship between the pattern of meniscal injury and changes in the biomechanical condition of the knee.

Tailoring the Thermal Conductivity of Rubber Nanocomposites by Inorganic Systems: Opportunities and Challenges for Their Application in Tires Formulation

The development of effective thermally conductive rubber nanocomposites for heat management represents a tricky point for several modern technologies, ranging from electronic devices to the tire industry. Since rubber materials generally exhibit poor thermal transfer, the addition of high loadings of different carbon-based or inorganic thermally conductive fillers is mandatory to achieve satisfactory heat dissipation performance. However, this dramatically alters the mechanical behavior of the final materials, representing a real limitation to their application. Moreover, upon fillers’ incorporation into the polymer matrix, interfacial thermal resistance arises due to differences between the phonon spectra and scattering at the hybrid interface between the phases. Thus, a suitable filler functionalization is required to avoid discontinuities in the thermal transfer. In this challenging scenario, the present review aims at summarizing the most recent efforts to improve the thermal conductivity of rubber nanocomposites by exploiting, in particular, inorganic and hybrid filler systems, focusing on those that may guarantee a viable transfer of lab-scale formulations to technological applicable solutions. The intrinsic relationship among the filler’s loading, structure, morphology, and interfacial features and the heat transfer in the rubber matrix will be explored in depth, with the ambition of providing some methodological tools for a more profitable design of thermally conductive rubber nanocomposites, especially those for the formulation of tires.

Three-Dimensional Force Prediction of a Flexible Tactile Sensor Based on Radial Basis Function Neural Networks

A flexible tactile sensor array with 6 × 6 N-type sensitive elements made of conductive rubber is presented in this paper. The property and principle of the tactile sensor are analyzed in detail. Based on the piezoresistivity of conductive rubber, this paper takes full advantage of the nonlinear approximation ability of the radial basis function neural network (RBFNN) method to approach the high-dimensional mapping relation between the resistance values of the N-type sensitive element and the three-dimensional (3D) force and to accomplish the accurate prediction of the magnitude of 3D force loaded on the sensor. In the prediction process, the k -means algorithm and recursive least square (RLS) method are used to optimize the RBFNN, and the k -fold cross-validation method is conducted to build the training set and testing set to improve the prediction precision of the 3D force. The optimized RBFNN with different spreads is used to verify its influence on the performance of 3D force prediction, and the results indicate that the spread value plays a very important role in the prediction process. Then, sliding window technology is introduced to build the RBFNN model. Experimental results show that setting the size of the sliding window appropriately can effectively reduce the prediction error of the 3D force exerted on the sensor and improve the performance of the RBFNN predictor, which means that the sliding window technology is very feasible and valid in 3D force prediction for the flexible tactile sensor. All of the results indicate that the optimized RBFNN with high robustness can be well applied to the 3D force prediction research of the flexible tactile sensor.