Thus, our newly developed "Tire Leap AI Analysis" utilises advanced AI-based analysis technology to analyse (for example) electron microscope imagery of tyre rubber compounds in order to achieve high-precision analysis that far exceeds human capabilities, thereby making it possible to derive accurate estimates of rubber properties from structural data found in this imagery.
Specifically, it is a technology that estimates rubber properties precise from combining data on the individual raw materials contained in a rubber compound with data on its internal structure. In the future, we will continue to develop this technology and develop technology to estimate the future rubber properties from electron microscope imagery of unused rubber.
■ Technology to Precisely Estimate Rubber Properties Based on Structures & Materials
Tire Leap AI Analysis utilises an AI-based image analysis system to analyse the internal structures of rubber in images captured by an electron microscope in order to infer information about the properties of the rubber based on its structural data (i.e. the results of image analysis). By combining this structural data with data about the materials that make up rubber compounds, this technology is then able to derive information about the physical properties of rubber with a high degree of precision.
■ Technology to Detect Changes in the Internal Structures of Rubber After Use & Estimate Resulting Changes in Rubber Properties
By comparing images of a tyre that has never been used (i.e. that is brand new) with images of a tyre that has been used (i.e. after wear over time), this AI-based image analysis system can determine where changes have occurred in the internal structures of the tyre’s rubber and then estimate the physical properties of the rubber in the areas that have undergone these changes. The practical application of this technology will facilitate the design of new rubber compounds that are less prone to performance degradation due to wear and tear, thus contributing to the development and advancement of Performance Sustaining Technology.
Dr. Miki Haseyama, Hokkaido University: We have developed a new AI technology that is able to estimate the extent of changes in the structures based on analysis of images of the internal structures of rubber. As compiling data for this kind of machine learning would otherwise be extremely time-consuming, one of the main merits of this new technology is the fact that this AI does not require prior field data from structural changes in rubber for machine learning. Rather, this AI uses deep learning to learn about the properties of new rubber (i.e. prior to undergoing structural changes) and then estimates the extent of changes in the structure by analysing how data from old rubber (i.e. after undergoing structural changes) compares to the data that it has previously learned about new data. This approach to machine learning allows the AI to automatically detect various types of changes in the structures of rubber.
Kiyoshige Muraoka, Senior Executive Officer, Sumitomo Rubber Industries: We have been working jointly with Hokkaido University to further advance the development of AI technology that can understand how the internal structures of tyre rubber change through use. We have already put this new technology to use in the development of our latest “ENASAVE NEXT III” fuel-efficient tyres, which not only achieve the highest possible “AAA-a” rating for fuel efficiency and wet grip performance (under Japan’s tyre labelling system), but also reduce changes in tyre performance that occur over time as a result of use by half. Moving forward, we will continue to advance our Tire Leap AI Analysis technology to find and analyse slight variations in the internal structures of rubber that human senses and knowhow have been unable to detect so that we can then use the resulting knowledge to develop new technologies that further enhance tyre performance and ensure that this high performance lasts longer. In this way, we will accelerate research and development toward producing high-performance tyres that provide greater safety and peace of mind with the aim of contributing to the realisation of a sustainable mobility society for future generations.
References:
Ren Togo, Naoki Saito, Takahiro Ogawa, Miki Haseyama, “Estimating regions of deterioration in electron microscope images of rubber materials via a transfer learning-based anomaly detection model,” IEEE Access, vol. 7, pp. 162395-162404, 2019.
Yokohama Rubber has established a new research and development centre in Hangzhou, China, as the Japanese tyre maker seeks to strengthen localised product development and speed up response times in the Chinese market.
The new facility, named Yokohama China Technical Center, began operations in May within the company’s new passenger car tyre plant in Hangzhou, which started production in November 2025.
The company said the centre would enable the local development of products specifically for the Chinese market, from initial research through to completion, helping to accelerate product launches and improve responsiveness to regional demand.
The centre will consolidate R&D functions for Yokohama Rubber’s tyre and multiple business divisions in China, while expanding engineering staff and testing facilities. Its activities will include tyre development, raw material analysis and evaluation, supplier audits, and mould drawing preparation.
Yokohama Rubber said the new operation would also support research into new raw materials and the development of local suppliers in China.
The company currently operates tyre plants in Hangzhou and Suzhou, alongside multiple business plants in Hangzhou and Weifang.
Aarika Innovation Launches KoolWheel Tyre Cooling System
- By TT News
- May 07, 2026
Chhattisgarh-based technology company Aarika Innovation has introduced KoolWheel, an automated tyre water spray cooling system manufactured in India.
The product is designed for freight vehicles and school buses to manage tyre overheating caused by road surface temperatures.
The system uses IR (infrared) temperature sensors, a 5-bar pump and solenoid valves to spray a mist on tyres when temperatures exceed a threshold. The hardware operates on a 12V setup and includes a controller that requires no driver intervention. Dashboard indicators and buzzers provide alerts regarding system status and temperature levels.
The company has introduced two variants of the product for KoolWheel Freight, which is designed for trucks, trailers and multi-axle vehicles, covering up to 22 tyres across six axles. And KoolWheel SchoolSafe, which is developed for school buses and coaches, featuring a 50-litre stainless steel tank and an automatic shutoff to prevent battery drain.
The company states the system can reduce tyre temperatures by up to 25deg Celsius and extend tyre life by up to 35 percent. The technology is intended to reduce the risk of blowouts and maintenance costs for fleet operators. The product is currently available in markets including Chhattisgarh, Madhya Pradesh, Maharashtra, Uttar Pradesh, Rajasthan and Telangana.
Swayam Agarwal, Founder, Aarika Innovation, said, “KoolWheel has been created to solve a very real problem faced by Indian transporters and school bus operators every day. Tyre overheating is not just a maintenance issue; it directly impacts road safety, operating costs, and fleet reliability. With KoolWheel, our aim is to offer an affordable, intelligent, and Made-in-India solution that helps fleets run safer, longer, and more efficiently.”
Pirelli Commences Cyber Tyre Production In Georgia
- By TT News
- May 07, 2026
European tyre major Pirelli is starting production of its Cyber Tyre technology at its plant in Georgia. The facility produces tyres for the US market, including products for the motorsport segment.
The announcement occurred during the SelectUSA Investment Summit. Cyber Tyre is a system that collects data from sensors embedded in tyres. This data is processed through software and algorithms to communicate with vehicle electronics. The system is intended to integrate with driving systems to provide functionalities for mobility and safety.
Pirelli is also introducing the Modular Integrated Robotised System (MIRS) at the factory. This manufacturing process uses robots to manage productivity and quality. The system creates a link between product design and application. This update is intended to increase the production capacity of the site.
The Georgia plant has operated for over two decades and includes a research and development centre. The facility uses natural rubber certified by the Forest Stewardship Council.
Claudio Zanardo, CEO of Pirelli North America, said, “The start of Cyber Tyre production in our Rome, Georgia plant is a significant milestone for Pirelli in this country. It reflects our commitment to bringing advanced technologies like Cyber Tyre closer to the market, further strengthening our industrial footprint and innovation capabilities in the United States.”
Yokohama Rubber Deploys AI And Simulation-Based Mould Design System
- By TT News
- May 04, 2026
The Yokohama Rubber Co., Ltd. developed a proprietary tyre mould design support system in April 2026, integrating finite element method (FEM) simulations and the company’s own artificial intelligence technology. This new tool is designed to augment the expertise of development personnel, enabling even less experienced staff to efficiently design moulds. It achieves this by providing data derived from numerous virtual experiments, which clarify how different mould design factors influence tyre characteristics.
The system accelerates mould development, lowers costs and minimises the rework typically associated with realising new designs. Furthermore, by fostering a multi-perspective understanding of the links between mould design elements and tyre performance, the tool equips Yokohama Rubber’s developers with fresh insights. These discoveries are expected to aid in creating tyres capable of achieving higher performance levels.
Developed under Yokohama Rubber’s HAICoLab AI concept launched in October 2020, the system addresses longstanding challenges. Mould design critically affects tyre traits, but traditionally understanding this relationship required expensive, time-consuming trial production and evaluations. The process also depended heavily on the tacit know-how of highly experienced staff, leading to variations in accuracy and development time based on individual expertise.
The support system resolves these issues through automated simulations and AI-based prediction and visualisation. It first generates numerous tyre FEM models with varied mould shapes and calculates their characteristics in a virtual space. These results train an AI surrogate model that instantly predicts design factor-performance relationships. By applying explainable AI technologies like SHAP and Partial Dependence Plots, the company’s developers can quantitatively visualise each factor’s impact, easily determining necessary adjustments to achieve targeted tyre characteristics.
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