Successful Completion of CenTiRe-Funded Research Project on Automated Vibration Mode Shape Characterization of Radial Tires
CenTiRe (Center for Tire Research), is pleased to announce the successful completion of a cutting-edge research project funded by titled Automated Vibration Mode Shape Characterization of Radial Tire using Zernike Moment Descriptor and Machine Learning. This pioneering research marks a significant advancement in tire technology by enabling more precise and automated characterization of tire vibration mode shapes, ultimately contributing to safer, more durable, and high-performing tires.
Led by Dr. Rakesh Kapania, a faculty member in the Department of Aerospace and Ocean Engineering at Virginia Tech, the project was driven by the innovative work of graduate student researchers, Sudharsan Parthasarathy, Junhyeon Seo, and Amir Meshkati. Their combined expertise and dedication have been instrumental in achieving the project’s objectives.
The goal of the research was to develop a robust and automated method for analyzing the vibration modes of radial tires, which are crucial for understanding how tires respond to various forces and conditions. To accomplish this, the research team employed Zernike Moment Descriptors – a mathematical approach for characterizing shape – combined with Machine Learning techniques to classify and analyze complex vibration patterns. This approach allows for the accurate detection of subtle changes in tire structures, which can provide manufacturers with valuable insights to improve tire design and enhance performance.? By automating the characterization of tire vibration modes, this project has opened the door to faster, more accurate tire testing methods that are critical for meeting the demands of modern transportation.
The Automated Vibration Mode Shape Characterization technology holds promise for various applications across the tire and automotive industries. By streamlining the process of analyzing tire responses to forces, the findings from this project could help manufacturers produce tires that are not only more reliable but also optimized for performance under various road conditions.
This research aligns with CenTiRe’s mission to support advancements in tire technology through collaborative academic-industry partnerships, and Virginia Tech’s commitment to pioneering research that addresses real-world challenges.
Virginia Tech and CenTiRe extend their gratitude to all industry member contributors involved in this research effort. The success of this project would not have been possible without the support and collaboration of everyone involved, particularly the dedication of Dr. Kapania, Sudharsan Parthasarathy, Junhyeon Seo and Amir Meshkati.
The main deliverable from this project research is a trained Convolutional Neural Network (CNN) model for classifying loaded and unloaded tire mode shapes using Physics-based data integrated through GUI (software). Other deliverables include final project reports, all test data and analyses, and journal publications, which are made available to CenTiRe industry members through the centire.org website. ??