Smart Finger: Integrating Triboelectric Sensing and Machine Learning

Robotic or automated manufacturing helps streamline workflow and advance manufacturing processes, allowing companies to remain globally competitive. One of the key factors in these processes is quality control which often requires validating or checking the materials used in the processes.

Smart Finger: Integrating Triboelectric Sensing and Machine Learning.

Image Credit: Shutterstock.com/ Willyam Bradberry

Now, researchers at the Beijing Institute of Nanoenergy and Nanosystems have developed a ‘smart finger’ that has the capacity to identify materials using “triboelectric” sensors that test its ability to gain or lose electrons, as well as determining other characteristics such its roughness, without the risk of causing damage. Published in the journal Science Advances, the team describes how they developed the triboelectric smart finger.

In principle, as each material has different capabilities to gain or lose electrons, a unique triboelectric fingerprint output will be generated when the triboelectric sensor is in contact with the measured object.

Dan Luo, CAS Center for Excellence in Nanoscience, Beijing Institute of Nanoenergy and Nanosystems

Machine Learning and Quantifying Material Parameters

Humans rely on haptic feedback as an essential sensory function when in direct contact or communication with the surrounding environment.

Tactile perception originates from the response of subcutaneous tactile corpuscles to different stimuli in the environment and the brain’s recognition of signals afferent through nerve fibers.

Dan Luo, CAS Center for Excellence in Nanoscience, Beijing Institute of Nanoenergy and Nanosystems

Typically, quantifying material parameters precisely at the psychological level of tactile perception can be a challenge when it comes to identifying the texture and roughness of a material. The smart finger developed by the researchers also uses machine learning to help improve the triboelectric tactile perception in the mechanism and aid human users of such systems.  

Moreover, the team has reported that they have developed a smart finger that has surpassed human tactile perception, enabling accurate identification of material type and roughness through the integration of triboelectric sensing and machine learning.

They claim the smart finger has at least 90% accuracy when sensing the material surface, which suggests that the technology has potential use in automating robotic manufacturing tasks, including materials sorting and quality control assessments.

Developing a Smart Finger

In recent years, varied efforts have been made to design sensors or devices with the capacity to identify materials based on various strategies, such as computer vision, thermal conductivity, ultrasound, etc. As a result, computer systems and robots are becoming increasingly proficient at interacting with the world around them, but they will also require a sense of touch before they can reach their full potential.

When tested across a varied number of samples, such as plastic, wood, silicon and glass, the smart finger demonstrated an accuracy average of 96.8% and at least 90% accuracy for all materials.

The system integrates machine learning-based data analysis with four small square sensors, each made of a different plastic polymer that was specifically chosen for its electrically conductive properties. The sensors are housed in a case that resembles a finger, hence the name ‘smart finger’.

When the sensors come into contact with an object’s surface, electrons from each square begin to interact with the surface in a different manner, which the team was then able to measure.

Each of the sensors is connected to a processor and organic light emitting diode (OLED) screen, which highlights the material type being assessed. In effect, the researchers were able to quantify tactile psychological parameters using the triboelectric effect, which could set a new paradigm when it comes to modeling human tactile perception.

Real-World and Future Scenarios

In a real-world scenario, the processor could be directly integrated into a manufacturing control mechanism. The smart fingers could then perform quality control checks and determine whether products are up to manufacturing standards.

Beyond the industrial/manufacturing setting, smart fingers could also be used in prosthetics as robotic limbs with a sense of touch to enhance manipulation techniques and the handling of objects.

The team also aims to introduce other sensors into the system, which include pressure, temperature, and humidity sensors, to help improve the tactile simulation.

In the future, artificial intelligence chips will be integrated into smart fingers to make them “smarter” and confer the ability to process data independent of the computer.

Dan Luo, CAS Center for Excellence in Nanoscience, Beijing Institute of Nanoenergy and Nanosystems

References and Further Reading

Qu, X. and Liu, Z., et al., (2022) Artificial tactile perception smart finger for material identification based on triboelectric sensing. Science Advances, [online] 8(31). Available at: https://www.science.org/doi/10.1126/sciadv.abq2521

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

David J. Cross

Written by

David J. Cross

David is an academic researcher and interdisciplinary artist. David's current research explores how science and technology, particularly the internet and artificial intelligence, can be put into practice to influence a new shift towards utopianism and the reemergent theory of the commons.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Cross, David. (2022, August 08). Smart Finger: Integrating Triboelectric Sensing and Machine Learning. AZoSensors. Retrieved on October 04, 2022 from https://www.azosensors.com/news.aspx?newsID=15138.

  • MLA

    Cross, David. "Smart Finger: Integrating Triboelectric Sensing and Machine Learning". AZoSensors. 04 October 2022. <https://www.azosensors.com/news.aspx?newsID=15138>.

  • Chicago

    Cross, David. "Smart Finger: Integrating Triboelectric Sensing and Machine Learning". AZoSensors. https://www.azosensors.com/news.aspx?newsID=15138. (accessed October 04, 2022).

  • Harvard

    Cross, David. 2022. Smart Finger: Integrating Triboelectric Sensing and Machine Learning. AZoSensors, viewed 04 October 2022, https://www.azosensors.com/news.aspx?newsID=15138.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit