Piezoelectric-Based Sensor Concept and Design with Machine Learning-Enabled Using COMSOL Multiphysics

Abstract

This paper presents the concept and design of a system that embeds piezoelectric sensors to measure the voltage of a mechanical load applied to it. COMSOL Multiphysics, a finite element simulation tool, was used to design the system and analyze the data to find a possible fingerprint of voltage changes. The sensors’ voltage readings were affected by the load applied to the surface of the structure with different magnitudes and speeds. The analyzed data show the effect of position and mass on the voltage readings and indicates the possibility of speed prediction. The obtained dataset results validated the concept of the proposed system, where the collected data can serve as a digital data pipeline model for future research on different artificial intelligence (AI) or machine learning (ML) modeling applications. From the obtained data, a reasonable view shows that voltage reading matrices can be utilized for the detection of vehicle speed, location, and mass if used as training data for machine learning modeling, which can benefit the Internet of Things (IoT) technology.

Machine-Learning-Based Sensor Design for Water Salinity Prediction: A Conceptual Approach

“…In this study, we use COMSOL Multiphysics, a powerful tool widely used in various fields such as energy harvesting, medical devices, and engineering [42][43][44], to design and analyze our sensor function and obtain the required data. Despite numerous research studies using COMSOL to test seawater sensors, the possibility of integrating machinelearning approaches with COMSOL Multiphysics for seawater sensor development has yet to be thoroughly explored [45,46].…”