Non-pneumatic tires (NPTs) have been widely used due to their advantages of no
occurrence of puncture-related problems, no need of air maintenance, low rolling
resistance, and improvement of passenger comfort due to its better shock
absorption. It has a variety of applications as in earthmovers, planetary rover,
stair-climbing vehicles, and the like. Recently, the unique puncture-proof tire
system (UPTIS) NPT has been introduced for passenger vehicles segment. The spoke
design of NPT-UPTIS has a significant effect on the overall working performance
of tire. Optimized tire performance is a crucial factor for consumers and
original equipment manufacturers (OEMs). Hence to optimize the spoke design of
NPT-UPTIS spoke, the top and bottom curve of spoke profile have been described
in the form of analytical equations. A generative design concept has been
introduced to create around 50,000 spoke profiles. Finite element model (FEM)
model is developed to evaluate the stiffness and damage-resisting performance of
NPT-UPTIS spoke. The FEM methodology has also been validated with average
accuracy of more than 95% for experimental vertical stiffness for commercial
NPT-Tweel. The stiffness and damage-resisting performance of generated designs
have been predicted with the help of machine learning regression models, which
were trained on the FEM results of 200 such designs. These 50,000 generated
designs have been categorized in four different categories based on different
level of stiffness and damage resistance performance. In this study, one
optimized design from each category has been selected and their performance have
been validated with 3D FEM simulation. It has been found that the suggested
topology optimization approach is efficient to generate UPTIS spoke designs with
having ±30% stiffness with 17%, 40%, and 56% more damage resistance performances
with respect to the starting reference design.
