Optimization of a non-pneumatic tire using design of experiments and machine learning techniques

Abstract

In this paper, the spoke structure of a non-pneumatic tire is optimized. The finite element model of the spoke structure is created and structural dynamic analysis is made using ABAQUS. The optimization goal is to simultaneously reduce three characteristics of the spoke structure, the mass, vertical displacement and acceleration. Simulations are performed according to the center composite design table, and the objective function is estimated using response surface analysis (RSA), random forest regression (RFR), gradient boosting regression (GBR) and artificial neural network (ANN). The genetic algorithm is used for minimization, and the optimization results of each objective function are analyzed through verification simulation. As a result of the optimization, the error between the predicted value and the actual value is 18.6% for RSA 3.1% for RFR, 1.7% for GBR, and 15.0% for the ANN. The ANN proposes the optimum design variables which derive output values smaller than the current minimum value because the ANN properly represents the nonlinear characteristics of the spoke model generated in this study.

Highlights

• Simulation based optimization of the spoke structure of a non-pneumatic tire

• Various regression models are used and their accuracy is compared

• A two-step genetic algorithm is used for optimization

KEYWORDS:

• Spoke structure
• finite element model
• optimization
• central composite design
• genetic algorithm

Acknowledgements

This study was supported by the Ministry of Science and ICT and the National Research Council of Science & Technology (NST) in 2021. (Development of core-technologies of high-safety non-pneumatic tires for industrial & agricultural vehicles).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.

Additional information

Funding

Sung Pil Jung reports financial support was provided by KATECH. Sung Pil Jung reports a relationship with KATECH that includes: employment. The co-author, Yeon Ok Kim is working for DaeDong Electric Vehicle Corp.