Artificial intelligence (AI) is steadily moving from experimentation to practical deployment in tyre manufacturing, where complex processes and variable raw materials often limit the effectiveness of fixed production standards. By analysing large volumes of plant data and responding to real-time process conditions, AI-driven optimisation systems are helping manufacturers improve efficiency, reduce waste and stabilise product quality. In an interaction with Tyre Trends, Vincent Barjaud of Braincube explains how such systems are transforming key production stages including mixing, extrusion and curing while complementing operator expertise.

The tyre industry indeed depends heavily on raw materials with significant variability, particularly those derived from natural sources and petrochemicals. These materials change over time and therefore are not always consistent in terms of quality and performance. Because industrial processes operate under constantly changing conditions, fixed production standards often create a hidden performance ceiling. Systems capable of adapting to real-time conditions allow plants to consistently reach the best achievable operating point.

Artificial Intelligence (AI) is helping manufacturers move beyond fixed production standards towards more adaptive approaches. Real-Time Process Optimisation (RTPO) processes historical and real-time plant data to continuously adjust operating setpoints based on live process conditions. By responding to variability in raw materials, equipment behaviour and operating environments, RTPO enables plants to consistently operate closer to their optimal performance point.

Speaking exclusive to Tyre Trends on the integration of AI, Technical Partner Manager at France-based Technology firm Braincube, Vicent Barjaud, said, “Our AI-driven solution provides real-time process optimisation by recommending the exact action operators should take, on which actuator and at what moment. Instead of suggesting a broad operating range, the system recommends the precise optimal value in real time. Because operating context evolve during production, this optimal value may change within hours. The system continuously adapts to these changes to maintain optimal performance.” The company devises solutions to address the entire tyre manufacturing process, but the software is particularly effective in compound mixing, extrusion and curing, where material transformation through machine actuation makes these stages highly process-oriented and suitable for optimisation.

The implementation typically takes six to twelve weeks from project kick-off to go-live. During this phase, plant data sources are connected and structured for AI analysis without requiring access to confidential compound formulations.

Since most industrial players maintain historical data through data historians, this data is injected into the system, enabling real-time optimisation and recommendations from day one, and in rare cases where no historical data exists, a few weeks are required to gather sufficient operational data.

The solution can be implemented in any plant equipped with PLC-based automation systems, while additional digital systems such as MES, ERP or LIMS improve recommendation accuracy, although valuable real-time operator guidance can still be delivered with only historian data and basic inputs.

ROOM FOR IMPROVEMENT

According to Barjaud, one of the biggest opportunities for improvement lies in the uniformity of the final tyre, particularly during quality control at the end of production. This is largely due to the curing stage.

“Plants often operate dozens of different curing moulds. Each mould functions as an individual asset, but many manufacturers treat them as if they were identical. In reality, each mould behaves slightly differently, which can affect tyre uniformity. Recognising and optimising these individual differences can significantly improve efficiency and product consistency,” he added.

It is considered beneficial to treat each curing mould individually because every mould has distinct characteristics including differences in lifetime, behaviour, wear patterns, maintenance history and the time since its last servicing.

When moulds are treated as identical, these variations are overlooked. By managing each mould separately rather than as part of a uniform group, process optimisation can be achieved more precisely, resulting in improved efficiency and performance.

“Strong optimisation results have also been observed in extrusion, where start-up phases of new process orders typically generate scrap as the first few metres of material are discarded before reaching a steady state. By adjusting process parameters more precisely, the time required to reach this steady state can be reduced, thereby lowering start-up waste,” noted Barjaud.

Braincube’s optimisation approach works similarly to navigation apps such as Waze or Google Maps, which continuously adjust routes based on real-time traffic conditions to reach a destination faster.

In the same way, Braincube dynamically updates manufacturing parameter recommendations as process conditions change. Similar to navigation applications such as Waze or Google Maps, the system continuously adjusts the optimal ‘route’ for the process as new conditions emerge.

The approach also applies to extrusion processes, where significant material waste often occurs during machine ramp-up. By helping operators set the correct parameters from the first seconds of operation, the company reduces the amount of material that must be scrapped at start-up.

INTO MANUFACTURING

Braincube works with tyre plant engineering teams to define ideal performance targets such as acceptable tyre uniformity ranges. It analyses production data to identify the actuators and operating conditions that drive optimal results and provides real-time insights to operators so processes can be adjusted to keep tyres within the desired ‘super zone’ of uniformity.

In mixing, its system addresses inefficiencies during product changeovers. Since the first batch after a changeover starts under different conditions such as temperature, roll distance and machine state, it separates the recipe for the first batch from subsequent batches, ensuring consistent viscosity and composition while reducing the higher scrap rate typically seen in the first batch.

For curing, Braincube performs real-time optimisation by adjusting parameters such as steam injection, temperature and curing duration based on the specific mould and its operating conditions. It also helps extend mould lifetime by identifying moulds that can safely operate beyond the usual maintenance threshold of around 3,000 tyres, potentially extending their life by 20–50 percent.

Overall, waste reduction comes from replacing fixed production standards with dynamic optimisation, where the system continuously analyses real-time conditions and recommends adjustments to recipes and operating parameters, improving efficiency while lowering scrap and environmental impact.

“In one case with a top-five global tyre manufacturer that deployed Braincube across its factories, we observed waste reduction of around 70 percent during the extrusion start-up phase. This level of improvement can significantly reduce both material losses and production costs,” noted Barjaud.

MACHINE NEEDS MAN

Braincube approaches root-cause analysis by identifying the drivers of success rather than only analysing defects. Instead of focusing solely on scrap and deviations, the system studies past production data to determine the conditions under which the best tyres were produced.

By analysing the highest-performance production runs including machines, operators, raw materials and process conditions, it identifies the key factors behind superior performance and recommends settings that help replicate those results consistently.

Installing Braincube mainly involves resolving material traceability across the plant. During a six-to-twelve-week integration phase, the system connects to existing data sources and reconstructs where each product was at specific times in the factory.

Once this mapping is completed, Braincube can continuously process data and perform automated optimisation. Plants with strong traceability systems integrate more easily, while others may require certain assumptions during setup.

“Our solution’s recommendations typically achieve more than 90 percent accuracy, but the system is designed to assist operators rather than automatically enforce actions. Operators receive recommendations but remain fully in control of whether to apply them. If a recommendation is rejected, the system immediately recalculates a new suggestion based on the updated operating conditions,” explained Barjaud.

He added, “This human oversight is important because some real-world conditions may not be captured in the dataset. For example, a lower operating temperature may have produced good results in the past because a machine door was open, affecting process conditions. If that factor was not recorded by sensors, the system may initially recommend the same temperature again even though the door is now closed. In such cases, operators can reject the suggestion, ensuring that AI insights are balanced with practical judgment.”

Barjaud contended that operator expertise remains essential when using AI systems. While the system provides data-driven recommendations, experienced operators play a critical role in deciding whether to apply them.

Their deep understanding of the process ensures that AI insights are used appropriately, making the combination of human expertise and AI analysis key to achieving the best production results.

IMPLEMENTATION AND SAFETY

The company also partners with machine manufacturers through white-label agreements, allowing them to offer Braincube-powered optimisation services alongside their equipment. This enables customers to benefit not only from the machinery itself but also from continuous performance optimisation.

In the tyre industry, Braincube currently focuses on mixing, extrusion and curing and still sees major opportunities to expand optimisation in these processes. Even when analysing a specific stage such as curing or tyre uniformity, the system incorporates data from upstream operations like building and other production steps to understand the factors affecting final performance.

The emphasis on optimisation ultimately centres on the final KPI, since this reflects what customers pay for, which is finished tyre quality and uniformity. By integrating data from across the entire plant including upstream processes and raw materials, Braincube helps manufacturers consistently meet required product performance standards.

Also, many tyre makers have more than one manufacturing unit. Integrating Braincube’s solution across each one requires a simple collaborative excursive involving the French company’s team and a ‘Champion’.

“Most companies appoint a champion or a dedicated engineer responsible for replicating successes across plants. This person ensures that the best practices identified in one plant are standardised and implemented across other facilities,” explained Barjaud.

He added that companies usually deploy Braincube as a technical solution while also establishing a human organisational structure to drive replication and standardisation. The combination of technology and internal leadership ensures that improvements are scaled across multiple plants.

Besides, data security is a top priority for Braincube, especially because industrial manufacturing data is highly sensitive. The system complies with major cybersecurity standards such as ISO 27001 and SOC 2, and in its 18 years of operation, it has never experienced a data breach.

The company regularly conducts external penetration tests, maintains a dedicated cybersecurity team and operates under the supervision of a Chief Information Security Officer (CISO) responsible for vulnerability management and system protection.

Regarding concerns about job replacement, Barjaud reported little resistance from engineers or operators. “Industrial environments have evolved through successive technological stages, from manual decisions to PLCs, closed-loop control, advanced process control and now AI. In this context, AI is generally viewed as the next step in improving efficiency, helping people make better decisions rather than replacing them,” he noted.

MARKET VIEW

Braincube operates globally with a full operational office in Europe but also has offices in United States and Brazil, which has supported the Latin American market for about 15 years.

From Europe, the company manages both European and Asian markets and works with several software distribution partners worldwide including in Thailand, India, Poland, Germany, Spain, Switzerland, UK and Italy, collaborating with firms such as Ematica to deliver and integrate its solutions.

In Asia, particularly in India and Southeast Asia, Braincube mainly relies on local partners rather than establishing its own offices. These partners, often industrial software distributors already working with automation systems, MES platforms and data historians such as AVEVA, handle integration and customer engagement.

The company is also engaging with new tyre manufacturers in Asia, typically through those partners who add Braincube’s AI-driven optimisation to their existing portfolios of PLC, SCADA and MES solutions.

Concluding the interaction, Barjaud pointed out that one of the biggest challenges for AI providers in the tyre industry is balancing multiple objectives such as throughput, energy consumption, material usage and product quality. n

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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.

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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.”

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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.”

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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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