Jeremias Neuhaus: “One of the biggest challenges in adopting data-driven solutions in the tyre industry is the availability of data and convincing customers not just of the solution itself but of the value of data transfer and real-time monitoring."

The tyre industry faces a significant challenge in data availability with fragmented supply chains, a lack of standardisation and minimal digital infrastructure limiting operational efficiency. Despite the growing importance of predictive maintenance, many manufacturers still rely on manual processes, while competitive concerns and regulatory restrictions further restrict data sharing. This absence of real-time insights hampers decision-making, making operations reactive rather than proactive. However, digitalisation and advanced data analytics are gradually reshaping the landscape. Solutions like HF Xplore aim to bridge this gap, offering real-time monitoring and predictive capabilities that can drive efficiency, sustainability and cost reduction.

Data availability is a challenge in the tyre industry due to fragmented supply chains, lack of standardisation and limited digital infrastructure. Manufacturers, distributors and recyclers operate in silos, making data consolidation difficult. Many businesses still rely on manual processes, while competitive concerns and regulatory restrictions further limit data sharing. Tracking end-of-life tyres (ELTs) is particularly challenging, impacting recycling efficiency.  

However, data availability is crucial for optimising operations, sustainability and innovation. Real-time data enables predictive maintenance, helping fleet operators reduce downtime and improve safety.

Accurate tracking of tyre usage and recycling supports circular economy initiatives and regulatory compliance. It also enhances research and development initiatives for advanced tyre materials such as electric vehicles and off-the-road (OTR) tyres, ensuring better performance and durability.

Improved data transparency can drive smarter decision-making, cost efficiency and sustainability in the industry. Digitalisation and data standardisation are key to overcoming these challenges.

However, HF Group’s Global Head of Digital Solutions, Jeremias Neuhaus, told Tyre Trends, “One of the biggest challenges in adopting data-driven solutions in the tyre industry is the availability of data and convincing customers not just of the solution itself but of the value of data transfer and real-time monitoring. Many decisions in the industry are still based on gut feeling rather than data-backed insights. Our focus is to bridge this gap by providing transparency into machine performance, enabling customers to make data-driven decisions instead of relying on intuition. By implementing real-time monitoring, we can significantly reduce downtime and help customers optimise processes. Even in the first step of implementation, simply visualising machine health and performance brings immediate value. Customers receive notifications for potential issues, allowing them to take preventive action before costly breakdowns occur.”

“The biggest opportunity in this space lies in the fact that data-driven insights can drastically improve operational efficiency. Once machines are connected and data is flowing, customers gain a much deeper understanding of the equipment, leading to better decision-making and optimised production cycles. Predictive maintenance and AI-driven analytics will further enhance operations by identifying potential failures before they occur. This is particularly crucial as manufacturers aim to reduce carbon emissions and energy consumption while increasing efficiency. Our approach stepwise towards AI-powered predictive solutions bring even greater efficiency and cost savings,” he added.

A critical concern in the tyre industry is data security and confidentiality, given how secretive manufacturers are about the respective production processes. “We address these concerns by focusing strictly on machine data rather than the proprietary tyre-making process. Our solutions do not need the actual process details to provide valuable insights. Additionally, the real, detailed data remains visible only to the customer, ensuring that they retain full control. In cases where AI-driven analytics are implemented, we collaborate closely with customers to develop models tailored to specific needs without compromising sensitive production data,” revealed Neuhaus.

The company launched the HF Xplore few years ago as a condition monitoring solution purely for curing presses. One of the significant developments following the merger of HF Mixing Group and HF Tire Tech Group has been the integration of previous initiatives into a joint project, creating a common condition monitoring solution. As a result, HF Xplore is now available for both curing presses and mixer lines. This expansion allows the company to offer real-time monitoring and predictive insights across two of the most critical processes in tyre manufacturing curing and mixing.

MONITORING TYRE FORMULATIONS

Curing and mixing are fundamentally different processes in tyre making, which presents a challenge in making HF Xplore compatible with both. The solution is to split monitoring into two layers viz-a-viz a common monitoring framework and machine-specific components.

The common framework includes monitoring cycle times, alarms, key performance indicators (KPIs) and production progress – elements that apply to both curing presses and mixer lines. However, each machine type has unique components that require dedicated monitoring.

For curing presses, especially electric-curing, HF Xplore focuses on monitoring hydraulic power unit and the electric curing, which is a crucial aspect of efficiency and quality control. On the other hand, for mixers, the system focuses on critical mechanical components such as the RAM movement, feeding mechanisms and drop doors, which are key areas that directly impact mixing performance and consistency. The drop doors, for instance, play a crucial role   in ensuring a smooth transition of rubber to the downstream process, making their monitoring essential for operational reliability.

The user interface of HF Xplore is designed to maintain familiarity across different machines. The dashboard layout remains consistent, so users who are accustomed to using it for curing presses will find a similar experience when working with mixer lines. This consistency reduces the learning curve and makes the system more intuitive for users handling both curing and mixing equipment.

Behind the scenes, the company is investing heavily in data modelling to refine and improve predictive capabilities. The company is developing individualised, flexible data models tailored to each machine type.

These models analyse operational patterns, detect anomalies and provide real-time insights to minimise downtime. By combining machine-specific expertise with data-driven intelligence, HF Xplore continues to evolve into a powerful predictive maintenance and performance optimisation tool for the tyre manufacturing industry.

INTO DATA MODELS

HF Xplore captures machine data but doesn’t analyse it directly. Instead, the system applies background logic to determine whether values are within acceptable limits. Currently, its primary function is real-time status monitoring, giving users an overview of machine condition. However, future iterations will introduce predictive maintenance capabilities, allowing companies to anticipate and address potential failures before they happen.

“At this stage, HF Xplore detects and predicts issues but does not provide specific solutions. As the technology evolves, it will go beyond identifying potential failures to offering actionable recommendations. This shift will help businesses move from reactive maintenance to a more proactive approach, reducing downtime and improving operational efficiency. To refine predictive maintenance, the system is being trained with large datasets in collaboration with customers. Over time, this will enhance the system’s accuracy, enabling it to not only flag potential issues but also suggest corrective actions,” informed Neuhaus.

AI and machine learning will play a central role in the company’s future roadmap, following a structured three-step approach – visualise, analyse and predict.

The first step will provide real-time machine status and process transparency. In the analyse phase, the company’s solutions will move beyond monitoring to offer deeper insights. The system will evaluate performance trends, identify operational limits and provide status feedback. The final phase will be where AI and machine learning take centre stage by analysing vast amounts of historical and real-time data. AI models will identify patterns, forecast failures and recommend preventive actions.

Commenting on whether implementing HF Xplore for a curing press or a mixing system presents different challenges, he said, “While both require detailed monitoring, mixing systems are more complex due to the interconnected components including upstream and downstream processes. Unlike curing presses, which operate as standalone units, mixing lines require data collection across multiple machines for effective monitoring. However, HF Xplore benefits from deep integration with its own equipment, leveraging PLC data to ensure seamless functionality across different systems.”

But for this to be a reality, different data models are pivotal. “The data model is essential for structuring and standardising the information displayed on the dashboard,” informed Neuhaus.

RETROFITTING HF XPLORE

HF Xplore is compatible with both greenfield and brownfield machines, though older models with outdated PLCs may have limitations. “While we cannot retrofit machines that are using old automation solutions, HF Xplore can be integrated into machines from the past few years, especially for condition monitoring. With electric-curing, it also enables precise tracking of electric curing performance, enabling deeper insights,” informed Neuhaus.

“One key challenge is data governance and security. Traditionally, machine data remained within the plant, but HF Xplore connects operational technology with information technology, raising concerns about data ownership and security. To address this, we have implemented user-based access controls, IP-based security and data encryption,” informed the executive.

For tyre plants with a mix of HF and non-HF machines, HF Xplore offers a custom dashboard creator with low-code functionality, allowing users to integrate and visualise data from different machines in just a few hours. A flexible data model further ensures standardised visualisation, even when machine types vary. While full integration with non-HF machines may require additional work, HF Xplore provides a comprehensive plant-wide monitoring solution for optimising performance.

“HF Xplore can potentially integrate with machines from other companies, but it depends on data accessibility and PLC compatibility”, contended Neuhaus, who highlighted the flexibility and modularity of their solution. 

As generative AI transforms the way consumers and businesses discover products, tyre manufacturers face a fundamental shift in digital marketing. Roshan Mohan, Co-Founder and CMO at FlowBlinq, and Founder of PCG argues that the next battle will no longer be fought on search engine rankings but on whether AI systems choose to recommend a brand in the first place.

For more than two decades, tyre manufacturers have refined their digital strategies around a familiar formula: optimise websites for search engines, invest in paid advertising, strengthen dealer networks and build visibility through reviews and comparison platforms. Success depended largely on securing a prominent position on Google’s search results.

That formula, however, is beginning to change.

The rapid adoption of generative artificial intelligence (AI) platforms such as ChatGPT, Gemini and Claude is reshaping how consumers search for information, compare products and make purchasing decisions. Rather than browsing multiple websites, customers are increasingly asking AI assistants to recommend the most suitable product based on their specific requirements.

For tyre manufacturers, this represents far more than another digital marketing trend. It fundamentally changes how products are discovered.

According to Roshan Mohan, Co-Founder and CMO at FlowBlinq, and Founder of PCG, companies that continue treating AI as simply another marketing channel risk missing a much larger transformation. “The customer journey for tyres has already started bending around AI, and the change over the next three to five years won’t be a redesign of the funnel; it’ll be a shift in where the funnel begins,” he says.

FROM SEARCH ENGINES TO AI CONVERSATIONS

Historically, buying tyres has been an information-intensive process. Consumers often compare technical specifications, dealer recommendations, user reviews, pricing and compatibility before making a purchase. Search engines have traditionally served as the starting point for that journey.

Generative AI is simplifying this process dramatically.  Instead of opening multiple browser tabs and manually comparing products, motorists can simply ask an AI assistant for recommendations based on vehicle type, budget, driving conditions and performance priorities. The AI then synthesises information from numerous sources into a single response. “What changes for the buyer is effort, not intent. They still want the right tyre for their car and budget, but instead of researching options and making the final comparison themselves, they are increasingly describing their needs to AI and letting it identify the best solution,” Mohan explains.

This shift effectively transfers much of the research process from the consumer to the AI model.

Industry forecasts suggest this transition is already underway. Gartner predicts traditional search engine volume will decline by 25 percent by 2026 as generative AI absorbs many queries that previously began with conventional search engines. Meanwhile, Checkout.com’s research indicates that consumers are embracing AI-assisted purchasing faster than many businesses are preparing for.

For tyre companies, the implication is profound: visibility may increasingly depend not on appearing first in search results but on being recommended within AI-generated answers.

THE RISE OF AI VISIBILITY

Search engine optimisation (SEO) has long centred on improving rankings through keywords, backlinks and domain authority. AI discoverability, Mohan argues, follows a very different logic.

“Traditional SEO was about ranking, winning a position on a page of 10 blue links. AI visibility, or Generative Engine Optimisation (GEO), is about being the answer rather than a link to the answer,” he says.

Unlike traditional search engines, large language models evaluate whether product information is sufficiently trustworthy, structured and complete before referencing it. If they cannot confidently interpret a manufacturer’s data, the brand may simply disappear from the recommendation altogether.

This places far greater importance on machine-readable product information than on conventional search optimisation. At the same time, AI systems are looking beyond a company’s own website to understand whether a brand is trustworthy. This makes it important for brands to have a presence across credible, independent sources, where editorial PR and genuine reviews can play a key role, rather than advertisements or advertorials. AI systems bring together these trust signals from multiple sources and present them to users in one place. This means the decision-making journey is increasingly shifting to the AI chat, where consumers can get a more comprehensive view before making a choice. Brands that build credibility across trusted sources will therefore be better placed to influence how AI systems recommend them.

Structured specifications, consistent product descriptions, schema markup and clearly organised technical information become essential because AI systems rely on these elements when generating responses.

FlowBlinq has developed what it describes as 17 Generative Engine Optimisation pillars to assess whether brands are sufficiently prepared for AI discovery. These include structured data quality, technical completeness and AI crawlability.

Perhaps more significantly, Mohan believes many companies have little understanding of how frequently AI platforms mention their products – or whether they are mentioned at all.

“Our citation tool runs a brand across ChatGPT, Claude and Gemini and shows, prompt by prompt, whether the brand gets cited, where it loses out to a competitor and where it’s simply absent from the answer altogether,” he adds.

TECHNICAL ACCURACY BECOMES A COMPETITIVE ASSET

Tyres differ from many consumer products because purchasing decisions depend heavily on technical specifications. Load index, speed rating, rolling resistance, wet grip, tread pattern and vehicle compatibility all influence suitability. Inaccurate recommendations can have genuine safety implications.

Mohan believes this makes structured product information particularly important for the tyre industry. “When product data is thin, a model doesn’t refuse to answer; it defaults to the brand it has the most confident, well-structured information about,” he adds. He warns that this tendency naturally favours manufacturers with richer digital product catalogues rather than necessarily those with superior products.

FlowBlinq’s research suggests considerable room for improvement. According to the company’s findings, 62 percent of Indian brand websites provide product descriptions that are insufficiently detailed for AI systems, while more than half lack product codes needed for accurate identification.

For tyre manufacturers, the solution is relatively straightforward but frequently overlooked.

Rather than embedding specifications within downloadable PDF brochures or image-based catalogues, companies should publish technical information directly on webpages in formats that AI systems can easily interpret.

Equally important is the broader digital reputation surrounding a brand. Mohan notes that AI systems increasingly rely on trusted third-party sources – including established news publications, Wikipedia and community platforms – to validate manufacturer claims before making recommendations.

AI ENTERS FLEET PROCUREMENT

The implications extend well beyond retail consumers. Business purchasing decisions often involve lengthy comparisons of performance, lifecycle costs, regulatory compliance and operational efficiency – precisely the type of structured analysis that generative AI performs well.

According to Mohan, procurement teams, fleet operators and original equipment manufacturers (OEMs) may adopt AI-supported purchasing even faster than retail buyers. “B2B tyre buying was never going to be immune to this, and it may move faster than consumer purchasing because procurement teams are exactly the audience generative AI tools were built to serve,” he says.

A fleet manager could ask AI to compare total cost of ownership across several tyre brands. An OEM purchasing team might request suppliers meeting specified rolling resistance or durability thresholds.

In such scenarios, manufacturers lacking accessible technical documentation risk exclusion before human procurement teams even begin formal evaluation. “The practical response isn’t a new sales deck. It’s making sure spec sheets, compliance documentation and comparative data exist in formats a model can read and trust,” Mohan says.

AI WILL ADVISE, BUT HUMANS WILL STILL DECIDE

While AI is poised to transform product discovery, Mohan believes the actual purchase decision will remain firmly in human hands – at least for high-value, safety-critical products such as tyres.

“I’d separate ‘AI helping me decide’ from ‘AI deciding for me’, because consumers still are the final decision makers,” he says.

Recent consumer research supports this view. While surveys indicate growing confidence in AI agents handling routine shopping tasks, willingness declines sharply when AI is expected to complete purchases autonomously. Most consumers remain comfortable with AI conducting research, comparing alternatives and shortlisting products but prefer to approve the final transaction themselves.

Tyres, Mohan argues, naturally fall into the category where human oversight will continue to matter.

“It’s a purchase people make infrequently, it carries real safety implications, and it typically involves a meaningful amount of money,” he says.

Consequently, AI is likely to dominate the research phase – evaluating specifications, warranty terms, prices and dealer options – while the final purchase decision remains with the customer.

However, one area where agentic commerce could quickly gain traction is in connecting customers directly with dealers. Rather than merely recommending a tyre, future AI assistants may also identify nearby retailers with available stock and book installation appointments automatically.

BECOMING AI-READY STARTS WITH THE BASICS

One of the most striking aspects of Mohan’s assessment is that the industry’s biggest challenge is not technological sophistication but digital housekeeping.

“It’s data, overwhelmingly, and it’s more basic than most companies expect,” he says.

FlowBlinq’s audits suggest that many corporate websites still lack the fundamental structure AI systems require. According to the company’s research, 91 percent of audited websites failed to provide clear information explaining their product catalogues in a way that AI could

understand. Even more concerning, nearly half were unintentionally preventing ChatGPT’s web crawler from accessing their websites because of security settings or plugin configurations.

“These aren’t strategic gaps; they’re operational oversights, and they’re fixable in weeks, not years,” Mohan claims.

For tyre manufacturers, this means that substantial improvements may not necessarily require major investments in new technology. Instead, they require a systematic review of how product information is organised, published and made accessible to AI systems.

Mohan also believes the next phase of digital readiness will involve preparing websites for agentic commerce by enabling real-time inventory visibility and ensuring AI systems can interact directly with product databases.

MEASURING RETURN BEYOND TRADITIONAL SEO

Digital marketing budgets have historically focused on search advertising, social media campaigns and marketplace optimisation. As AI-driven referrals grow, Mohan argues that businesses should begin allocating dedicated budgets towards AI discoverability.

“Yes, and the case for it is measurable rather than speculative now,” he says. Rather than relying solely on website traffic or keyword rankings, he believes organisations should monitor a different set of performance indicators.

Among the most important are how frequently AI systems cite a brand when responding to relevant queries, whether those citations are accurate and whether visitors arriving through AI recommendations convert differently from those originating through conventional digital channels.

Adobe’s Digital Insights research suggests AI-generated referrals are not only increasing rapidly but also producing stronger conversion rates than traditional referral sources. According to Mohan, this reflects the higher purchase intent of consumers who have already completed much of their evaluation through AI before visiting a manufacturer’s website.

TRUST WILL DETERMINE INFLUENCE

The emergence of AI recommendations inevitably raises questions about transparency. If AI systems become influential in shaping purchasing decisions, how can brands improve visibility without manipulating results?

For Mohan, the answer lies in accuracy rather than optimisation. “The honest answer is that AI-powered recommendations only work for a brand in the long run if they’re accurate, because these systems increasingly get checked,” he explains.

He believes manufacturers should resist the temptation to game AI systems through exaggerated marketing claims.

Instead, success will depend upon providing complete, verifiable product information that allows AI to make fair comparisons based on genuine performance characteristics.

“So the lever isn’t gaming a model into over-recommending you. It’s making sure that when a model compares your tyre honestly against a competitor on wet grip, rolling resistance or price, your data is complete enough that you win the comparisons you’re actually built to win,” he says.

In his view, transparency is not a constraint on AI marketing but its most durable competitive advantage.

FROM RECOMMENDATIONS TO TRANSACTIONS

The next evolution extends beyond recommendations. Emerging protocols are enabling AI systems to communicate directly with commerce platforms, inventory databases and pricing systems, allowing them to perform increasingly sophisticated purchasing tasks.

According to Mohan, this represents a significant opportunity for tyre manufacturers and dealers.

“The interesting shift is that AI agents are starting to interact with commerce systems directly... rather than just reading a webpage and stopping there,” Mohan says.

Once connected to live inventory systems, AI assistants could recommend the exact tyre that fits a customer’s vehicle, confirm stock availability at nearby dealers and compare prices in real time. Now, it can also make purchases directly from the chat window. This is something FlowBlinq is uniquely positioned to address as well.

Rather than generic recommendations based on previous purchasing patterns, personalisation could become highly contextual – considering vehicle compatibility, driving conditions, current inventory and even maintenance priorities.

However, Mohan cautions that these benefits will only be realised by organisations whose internal systems can support such interactions. Manufacturers and retailers will need modern, connected back-end infrastructure capable of sharing real-time inventory and pricing information with AI platforms.

ENGINEERING PRODUCTS – AND ENGINEERING DISCOVERABILITY

Looking ahead, Mohan does not believe AI will replace product quality as the defining competitive factor. Instead, he sees AI readiness becoming an equally important complement to engineering excellence.

“Product quality will always be table stakes; nobody wins on AI visibility with a mediocre tyre,” Mohan says. Yet he argues that superior products alone may no longer guarantee commercial success.

As purchasing journeys increasingly begin with AI conversations rather than search engines, brands that fail to present their technical information in formats AI systems can retrieve and trust may simply disappear from consideration.

“The winners will be the manufacturers who treated AI readiness as seriously as they treat product engineering,” Mohan says. He returns to Gartner’s prediction of declining traditional search volumes not as a warning but as an indication of how rapidly digital discovery is evolving.

“The discovery layer is moving to AI faster than most manufacturers’ data infrastructure is moving with it,” he says.

His concluding observation perhaps best captures the industry’s emerging challenge.

“A brand can make the best tyre in its category and still lose the sale simply because it was invisible in the one conversation the buyer had before deciding. That’s a genuinely new way to lose, and avoiding it is now a core marketing responsibility, not a technical footnote,” Mohan says.

AI mobile data capture company Anyline has released the latest version of TireBuddy, a smartphone-based system for automotive tyre inspections. Version 1.8 introduces fully automated sidewall capture that removes human variability from data collection. The tool has already helped service teams achieve faster, more uniform inspections over the past year, leading to increased tyre sales and stronger customer trust.

The automated mechanism uses on-device guidance that evaluates each image against four criteria: full sidewall detection, sharpness, proper distance and angle and overall clarity. This real-time feedback minimises redo scans by guiding technicians to capture optimal images immediately. The system addresses common challenges in busy service bays where accuracy often suffers due to varying experience levels.

Standardisation of inspection quality is a primary benefit, as consistent results are achieved regardless of who holds the phone. This removes dependency on technician skill or training duration. New or seasonal staff can perform scans confidently from day one without extensive instruction. The automated capture now serves as the standard protocol for all inspections across locations and shifts.

Additional features include tyre mismatch alerts that flag size discrepancies, automated email reports to back-office systems and a redesigned results screen consolidating sidewall information and tread measurements. With hundreds of thousands of annual inspections, this update reinforces TireBuddy's role in modernising tyre service operations.

Lukas Kinigadner, CRO, Anyline, said, “A shop is only as consistent as its least experienced inspector. Automated sidewall capture gets every scan to the same standard, so teams can stop treating inspection quality as a variable.”

Epson, a global leader in SCARA robot manufacturing, has unveiled its next-generation industrial robotics portfolio at Automation Expo Mumbai 2026. The newly introduced lineup features the high-end CX-A Series 6-axis robots, the LS-C Series SCARA robots, the RC+ 8.0 programming software and the advanced SafeSense safety technology, all designed to address diverse manufacturing applications such as pick-and-place, precision assembly, parts transfer and material handling.

The new offerings significantly expand Epson’s existing industrial robotics family, which already includes the 6-axis C-Series and SCARA T-Series and LS-Series models with payloads ranging from 3 to 20 kilogrammes. With the addition of the CX-A and LS-C Series, manufacturers across various sectors can achieve heightened productivity, flexibility and operational efficiency. The CX-A Series is engineered for complex tasks with a payload capacity of up to seven kilogrammes and a reach of 900 millimetres, available in IP67, cleanroom and ESD variants, while the LS-C Series provides a compact SCARA platform with a 50-kilogramme payload, a 1,000-millimetre reach and cycle times as fast as 0.298 seconds.

Complementing the hardware, the RC+ 8.0 software offers an integrated environment for programming, simulation and system management, facilitating faster automation deployment with support for Visual Studio and C++ development. Additional efficiency features include enhanced diagnostics, OPC UA, GUI builder and safety functions, alongside co-creation tools like Library Builder and RC+ Extension. Meanwhile, the SafeSense technology promotes safer human-robot collaboration by incorporating Safety Limited Speed and Safety Limited Position functions, which can potentially reduce the need for extensive safety fencing and thereby increase operational flexibility.

With over four decades of industrial robotics expertise and more than 200,000 robotic arms deployed globally, Epson continues to drive operational excellence for businesses. Attendees at Automation Expo Mumbai 2026 have the opportunity to view live demonstrations of these solutions and consult with Epson specialists about transforming their manufacturing operations.

Siva Kumar, Sr General Manager – Sales and Marketing, Epson India, said, "India is rapidly emerging as a global manufacturing hub, and automation will play a pivotal role in shaping its future. With our new industrial robot lineup and RC+ 8.0 platform, Epson is delivering the speed, precision and intelligence manufacturers need to compete in an increasingly dynamic marketplace. We remain committed to enabling businesses to accelerate automation adoption and build smarter, more agile and globally competitive manufacturing operations."

A consortium of Fraunhofer institutes has reached a key milestone in the Technology Platform for Tire Abrasion and the Identification of its Emissions in Road Traffic (TERIS) project, moving closer to establishing standardised laboratory methods for generating, analysing and predicting tyre wear.

The project, led by the Fraunhofer Institute for Structural Durability and System Reliability LBF, together with Fraunhofer ICT, Fraunhofer IGD and Fraunhofer IWM, aims to provide the tyre industry, testing organisations and environmental agencies with reliable and practical laboratory procedures for assessing tyre abrasion emissions.

The first project milestone has been completed following a successful review by an advisory board comprising industry experts.

The consortium has developed reference methods for tyre abrasion, particle analysis, tribological modelling, artificial intelligence-based surface analysis, a laboratory test bench concept, accelerated ageing techniques and volatile organic compound (VOC) detection.

According to the consortium, combining different particle collection and measurement techniques enables more precise analysis of both airborne and deposited tyre wear particles. At the same time, tribological models have been developed to better understand the relationship between loading conditions, material properties, surface structures and particle formation, allowing real-world tyre wear processes to be replicated under laboratory conditions.

Researchers have also developed a specialised test chamber for accelerated ageing, enabling tyre samples to be exposed to controlled environmental conditions before evaluating their abrasion behaviour.

Another development is an optical detection system that uses artificial intelligence to identify and classify surface structures. The system has been validated using substitute materials and is expected to be applied to rubber samples during the next phase of the project.

The consortium has also designed a laboratory test bench that combines multiaxial loading, controlled generation of tyre wear particles, targeted particle collection and integrated optical sensors within a single testing platform.

In addition, the project combines accelerated weathering with chemical analysis of volatile organic compounds released from tyre abrasion to assess the environmental impact of tyre wear particles.

The researchers said the work will provide the foundation for faster and more practical laboratory evaluation of new rubber compounds. The resulting methods are intended to help tyre manufacturers reduce emissions, accelerate product development and support compliance with the requirements of the Euro 7 standard.

At Fraunhofer IWM, researchers focused on refining tribological wear models and friction surface concepts to simulate particle formation under controlled laboratory conditions. The institute designed a parameterisable wear test that studies friction between plate materials and model surfaces with different structures, enabling researchers to investigate the mechanisms responsible for particle generation.

Initial findings indicate that tyre wear results from multiple interacting mechanisms rather than a simple relationship between particle emissions and factors such as speed, contact force or temperature. The researchers collected and analysed particles across a wide range of sizes during the study.