As the only automotive part in contact with the road, tires play a major role in satisfying the various performance requirements for cars.
Called the arrival of CASE, the mobility society has reached a major turning point, and there is a demand for further advancement of automobile tires.
Tires need to rapidly achieve required performance and functionality shifts to support “the evolution of mobility.” To achieve this, high precision and high speed in design are extremely important.
AI design support technologies have been incorporated into the conventional T-mode (Tire Structure Analysis = Tire Simulation, Vehicle Behavior Analysis = Driving Simulation), evolving toward more advanced tire development processes with the new “T-MODE.”
By newly implementing a 6th generation HPC system (High Performance Computing System), processing power has quadrupled, machine learning data is automatically generated, and Massively Parallel Processing Technology and multiplex processing has been enhanced.
We analyze and predict the aerodynamic characteristics of tires and vehicles with rolling tires taking into consideration of tire shape changes under various conditions.
The new T-MODE platform unifies management of various data types as shared assets and allows for data sharing between engineers.
Data from simulations run by engineers is stored automatically on a shared storage server, and is used in new analysis and prediction as database assets, which can lead to efficient and high-performance design.
The new T-MODE platform unifies management of various data types as shared assets on a shared storage server.
Connecting design, simulation and test data improves its added value and allows for its usage as data for machine learning.
Conventionally, the design specifications are inputted and then a simulation is performed to obtain tire characteristic values.
If characteristic values do not satisfy requirements, the simulation is repeated, extending the process term overall.
With T-MODE, which uses SPDM, if required performance values are entered, tire characteristic values can be predicted in real time through machine learning.
By improving simulation technologies used in the conventional T-mode, and due to the construction and introduction of SPDM, machine learning has become possible. As a result, T-MODE makes it possible to develop higher performance tires more efficiently.
Toyo Tires will continue to take tires to higher levels by applying our "design technologies for future mobility".