Nexen Tire stated that it has developed an Artificial Intelligence (AI)-based tyre performance prediction system.
Nexen Tire Improves Product Performance with Artificial Intelligence (AI)
The company explained that it will use a machine learning technology in the concept design stage to precisely and rapidly anticipate the primary performance indicators such as fuel efficiency, noise, handling process, etc. that are considered during the tyre development process by establishing a tyre performance prediction system using AI technology.
Since securing high-quality data in big volumes is critical for machine learning, Nexen Tire has created a data pre-processing technology that can detect and replace irregularities in the protected data. By securing a substantial amount of learning data utilising data augmentation techniques, the company was able to secure a forecasting model with good predictive performance for insufficient data.
The ability to forecast tyre performance early in the tyre development process has a significant impact on the quantity of prototypes produced and the development time. Primarily, Finite Element Analysis (FEA) is used to forecast the performance of generic tyres. With FEA, the shape and material properties of a tyre can be modeled as a virtual three-dimensional tyre on a computer, and the mechanical properties of a product can be confirmed through numerical calculation. The advantage of FEA is that it can evaluate high precision performance estimates, but it takes a long time to calculate the figures, therefore developers quickly analyse performance at the concept design stage, which is inefficient.
Nexen Tire has been consistently working on establishing a Virtual Product Development System. With the newly developed tyre performance prediction system using AI technology, this will allow faster and more accurate tyre design and performance improvement during the pre-production process, in addition to the existing FEA-based performance prediction technique and Genetic Algorithm that suggest optimal design plans.
