Bridgestone Europe

Methods for determining the position of a tire of a vehicle are disclosed. The methods comprise obtaining an indication of a load of the tire, detecting a variation of the indication over time and obtaining an information of a lateral acceleration of the vehicle. It is determined, based on the detected variation of the first indication and the information of the lateral acceleration, whether the tire is mounted on the left or right side of a longitudinal axis of the vehicle. Corresponding apparatuses, computer programs and vehicles are also disclosed.

A tire inspection system includes a rotation assembly configured to selectively couple with a tire and rotate the tire about an axis of rotation, at least one sensor configured to facilitate acquiring vision data of the tire, and one or more processing circuits. The one or more processing circuits are configured to receive the vision data of the tire, generate, based on the vision data, a graphical representation of the tire for display on a user interface, determine, based on an input from a user to the user interface, an identification of a feature on the graphical representation, the anomaly on the graphical representation being indicative of the feature on the tire, and determine, based on the identification of the anomaly, whether to perform a maintenance action on the tire.

A computer implemented method (100) for assessing the impact of vehicle usage on wear of at least one tire (2) on a first vehicle (1), comprising: obtaining data indicative of the load acting on the tire (2), and of the longitudinal acceleration, lateral acceleration, and speed of the first vehicle (1); calculating tire wear energy index components for the least one tire (2) based on the obtained data. The tire wear energy index components comprise one or more of: cornering components; braking components; and positive longitudinal acceleration components. The method (100) comprises obtaining historical data comprising the same tire wear energy index components for tires on the first vehicle (1) and/or on another vehicle; calculating an average value for each tire wear energy index component in the historical data; comparing the calculated tire wear energy index components for the at least one tire (2) with the calculated average values; and generating an output based on the comparison.

An adhesive composition for a surface of a pneumatic tyre and use thereof, tyre comprising the adhesive composition and a method of applying the adhesive composition.

This disclosure is generally directed to computer-implemented methods and apparatus for calculating and/or monitoring a tire wear rate of a vehicle. In one embodiment, a computer-implemented method for determining tire wear of at least one tire mounted to a vehicle is provided. The method comprises obtaining a vehicle speed information of the vehicle, v<vehicle>, obtaining tire movement data from at least one sensor mounted to the at least one tire or at least one rim of the vehicle, and determining the tire wear based on the tire movement data and vehicle speed information.

A process for rubberising an electronic device for a tyre and a rubberised electronic device obtained or obtainable therefrom, the process comprising coating the electronic device with a liquid composition.

A rubber composition for a pneumatic tyre comprising a polymer having a low glass transition temperature and a blend of hydrocarbon resins, and a pneumatic tyre produced therefrom.

A rubber composition for a pneumatic tyre comprising a polymer having a low glass transition temperature and a blend of hydrocarbon resins and pneumatic tyre produced therefrom.

A rubber composition for a pneumatic tyre comprising a polymer having a low glass transition temperature and a blend of hydrocarbon resins and pneumatic tyre produced therefrom.

The present disclosure relates to determining the stress response of a viscoelastic material. More particularly, the present disclosure relates to computer-implemented methods for training a model to determine the stress response of viscoelastic material, methods for training a machine learning model to determine the viscosity of viscoelastic material, methods for generating augmented training data for determining the stress response of viscoelastic material, as well as to corresponding devices and computer programs. The methods, devices and computer programs as disclosed herein may be used for designing and manufacturing a tire.