Dual-Modal Material Identification Method via MTEG-TENG Synergistic Sensing and Machine Learning Optimization in Multiple Environments

Abstract

Material identification sensors, as the core components that endow robots with intelligent perception capabilities, are crucial for their development and innovation. However, the complexity and diversity of environmental conditions pose greater challenges to the accuracy of sensors in identifying materials. This paper proposes a dual-modal collaborative material identification method based on microthermoelectric generator (MTEG) and triboelectric nanogenerator (TENG). A prototype is fabricated which consists of a thermal tactile material identification unit (TT-IU) based on MTEG and a contact electrification material identification unit (CE-IU) based on TENG. The TT-IU measures voltage induced by the difference in temperature between its two ends, reflecting the material’s thermal diffusivity. The CE-IU measures voltage produced when materials contact with the unit, indicating the electron affinity of materials. Since individual material has distinct thermal diffusivity and electron affinity, the classification of materials can be achieved by correlating and analyzing these two independent voltage data. To verify the material identification capability of this method, a MTEG-TENG dual-modal collaborative characteristic material identification performance validation experiment system is set up. Furthermore, this paper delves into the impact of external conditions and contact conditions such as contact pressure, material surface roughness, ambient temperature and humidity on recognition performance. The experiment results indicate that under open conditions, the material identification method can significantly distinguish between materials. Integrated with machine learning techniques, the material identification method achieves identification of eight characteristic materials under various external conditions with an overall identification accuracy of 93.54%.