Tire Uniformity via Multivariate Normal Distribution Analysis
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Solution Overview
Problem
Conventional tire building methods often result in non-uniformities that cause vibrations and discomfort during vehicle operation, as they fail to effectively measure and address the composite uniformity parameters of tires, which are critical for ride comfort.
Innovation Solution
A method and system that utilize multivariate normal distributions to analyze uniformity data from multiple harmonics of tire uniformity parameters, determining characteristics such as peak-to-peak range and amplitude distribution to improve tire uniformity by modifying manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional tire building methods are used, then manufacturing simplicity is maintained, but tire uniformity deteriorates resulting in vibrations and discomfort
Solution Approach 1:
The patent applies preliminary action by measuring uniformity parameters during the tire manufacturing process and using multivariate normal distribution analysis to predict composite uniformity characteristics before the tire is complete. This allows manufacturing adjustments to be made proactively to ensure uniformity requirements are met, rather than detecting and correcting non-uniformities after the fact.
Solution Approach 2:
The patent employs parameter changes by analyzing multiple uniformity parameters (radial force variation, lateral force variation, tangential force variation, radial runout, lateral runout) and their harmonics using multivariate normal distributions. This statistical approach transforms individual parameter measurements into a composite uniformity prediction, enabling more precise control of tire uniformity through data-driven manufacturing adjustments.
2Measurement precision
If multiple uniformity parameters and harmonics are measured and analyzed, then tire uniformity prediction accuracy is improved, but measurement and data processing complexity increases
Solution Approach 1:
The patent applies merging by combining multiple uniformity parameter measurements and their harmonic components into a single composite uniformity prediction using multivariate normal distribution analysis. This integration consolidates complex multi-dimensional data into unified statistical parameters that predict overall tire uniformity, simplifying the interpretation of extensive measurement data.
Solution Approach 2:
The patent replaces complex mechanical analysis and manual evaluation of multiple uniformity parameters with statistical computation using multivariate normal distributions. This substitution of mathematical modeling for mechanical interpretation streamlines the analysis of harmonic data and enables automated prediction of composite uniformity characteristics from measured parameters.
3Object-affected harmful factors
If composite uniformity parameters are identified using multivariate normal distributions, then ride comfort is improved by reducing vibrations, but computational requirements increase
Solution Approach 1:
The patent applies preliminary action by performing multivariate normal distribution analysis and composite uniformity prediction during the manufacturing process, before the tire is delivered to the vehicle. This proactive computational approach identifies and corrects uniformity issues that would cause vibrations, preventing the transmission of harmful vibrations to the vehicle and occupants rather than addressing them after deployment.
Data Source
AI summary
Methods and systems for improving the uniformity of a tire are provided. More specifically, one or more characteristics of a composite uniformity parameter can be determined from uniformity summary data (e.g. uniformity vectors) associated with a plurality of harmonics of the composite uniformity parameter. For instance, a peak to peak range of a composite uniformity parameter and/or a distribution of amplitudes of a composite uniformity parameter for a set of tires can be determined from uniformity vectors associated with selected harmonics of the composite uniformity parameter. According to example aspects of the present disclosure, the one or more characteristics of the composite uniformity parameter can be determined using multivariate normal distributions (e.g. bivariate normal distributions) of the uniformity summary data. Once identified, the one or more characteristics can be used to modify tire manufacture to improve tire uniformity.