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

TL;DR

This paper presents an integrated framework combining generative design, machine learning, and optimization algorithms to enhance non-pneumatic tire structures, achieving significant improvements in stiffness, durability, and vibration reduction.

Contribution

It introduces a novel combined approach for optimizing non-pneumatic tire geometries using polynomial parameterization, machine learning, and advanced optimization techniques.

Findings

53% stiffness tunability achieved

Up to 50% improvement in durability

43% reduction in vibration levels

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.