Multi Objective Design Optimization of Non Pneumatic Passenger Car Tires Using Finite Element Modeling, Machine Learning, and Particle swarm Optimization and Bayesian Optimization Algorithms

2026-02-04

AI Summary

This study addresses the challenges of stiffness tunability, poor durability, and high-speed vibration inherent in non-pneumatic tires due to their discontinuous spoke structures. To overcome these limitations, the authors propose an efficient multi-objective optimization framework that integrates generative design with machine learning surrogate models. Specifically, they parameterize the UPTIS-type spoke profile using high-order polynomials and employ PCHIP interpolation to generate approximately 250 geometric configurations. Costly finite element simulations are replaced by kernel ridge regression (KRR) and XGBoost surrogates, while particle swarm optimization and Bayesian optimization are synergistically combined to explore the design space. The resulting optimal design demonstrates significant improvements—53% in stiffness tunability, 50% in durability, and 43% in vibration suppression—substantially outperforming the baseline and validating the efficacy and novelty of the proposed approach.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Non Pneumatic tires offer a promising alternative to pneumatic tires. However, their discontinuous spoke structures present challenges in stiffness tuning, durability, and high speed vibration. This study introduces an integrated generative design and machine learning driven framework to optimize UPTIS type spoke geometries for passenger vehicles. Upper and lower spoke profiles were parameterized using high order polynomial representations, enabling the creation of approximately 250 generative designs through PCHIP based geometric variation. Machine learning models like KRR for stiffness and XGBoost for durability and vibration achieved strong predictive accuracy, reducing the reliance on computationally intensive FEM simulations. Optimization using Particle Swarm Optimization and Bayesian Optimization further enabled extensive performance refinement. The resulting designs demonstrate 53% stiffness tunability, up to 50% durability improvement, and 43% reduction in vibration compared to the baseline. PSO provided fast, targeted convergence, while Bayesian Optimization effectively explored multi objective tradeoffs. Overall, the proposed framework enables systematic development of high performance, next generation UPTIS spoke structures.

Problem

• Non-Pneumatic Tires
• Stiffness Tuning
• Durability
• High-Speed Vibration
• Spoke Geometry

Innovation

• Non-pneumatic tires
• Generative design
• Machine learning surrogate models
• Multi-objective optimization
• Bayesian Optimization