Tire manufacturing is one of the tiresome and specialized jobs in this industrial age; however, with the help of artificial intelligence (AI), tire manufacturing can be simplified and achieve productive results with less effort and less human involvement.
Traditionally, tire manufacturing is very labor-dependent, from research, testing, layer designing, tread patterns, and every other process is executed by human-dependent machinery or humans themselves. This is still the case.
The AI is revolutionizing different industries and sectors all over the world. The most impactful change can be seen in the marketing sector. The AI tools are created that do all the job better than ten years of experienced marketers can do, just by some clicks.
Similarly, predictive AI tools are game-changing for individual finances; instead of paying heavy fees to stock brokers, these AI tools predict more profitable portfolios for individuals than any best broker can do.
AI in tire manufacturing industries has also shown significant results; machine learning techniques can predict how the tire will wear off, what conditions are not proper to drive, and how a little tweak in tread pattern will increase tire durability.
We will talk about all these in detail about the challenges, future, and benefits of implementing AI technologies in the tire industry. So keep reading this article if you are an engineer working in a tire manufacturing site, owner of a tire manufacturing company, or interested in a revolution in the tire industry because it will help you make better choices for the future.
Table of Contents
Traditional Tire Manufacturing
Traditionally, tire manufacturing has been time-consuming, tiring, and labor and machinery-intensive, and it goes around most manufacturing industries. Every part of the activity is either done by humans or human-dependent machinery, which is costly, time-consuming, and space-taking activity.
The tire manufacturing process starts from years of research due to the improvements every tire requires, collecting raw materials, converting them into rubber compounds, making tire layers via fabrics, steel belts, and other components, and then vulcanizing and transforming all that into the tire.
Even after all this time and effort, the tire does not come out as a total masterpiece because of a little misjudgment in the first research stage. The whole tire innovation will be a failure, with the waste of money following liabilities if a tire failure causes trouble to the driver.
That is where AI helps the manufacturers; with the help of predictive AI, there is no need to judge or predict without probable results. The AI prediction will be based on proofs and tested evidence. It will show what raw material will work with different combinations and which compound will suit a combination of conditions.
So, instead of testing, manufacturers can pick the right combination of materials and come up with the best possible tire without proposing years of research.
AI Revolutionizing the Industry
The whole manufacturing industry is revolutionizing after artificial intelligence, and the tire industry is also among them.
The machine vision system is an example of AI help in tire quality control; with the help of a camera and AI algorithm, systems detect inconsistencies among tire built, faults like bubbles, unshaped tires, sidewall defects, and other minor imperfections that human eyes might not identify.
Similarly, tire component monitoring is possible due to the real-time tracking AI. Sensors detect a deviation in material composition so that the rubber compound meets the standards. This system reduces any chance of faults by manufacturers so the customers get the highest quality and safe vehicle tires.
Predictive Analytics for Machinery Health
From a manufacturing point of view, AI is also helping in predicting machinery health and remaining life. With the help of historical machinery data, AI can predict the remaining machinery’s life, allowing manufacturers to perform maintenance tasks and any repairs if needed.
With that, it also reduces maintenance costs and optimizes machinery performance. Manufacturers can save money by repairing parts promptly so that no enormous damage occurs and only perform maintenance when needed.
AI-Driven Material Selection and Allocation
AI is applied to optimize material selection and allocation in tire manufacturing. Algorithms can analyze the properties of different rubber compounds and determine the ideal blend for specific tire models. Fuel efficiency, durability, and performance are optimized while reducing inventory costs.
With the help of optimized resources, the environmental impact can also be reduced. Because the tire is built with the minimum materials without waste, it won’t impact the environment as much as other manual methods of material requirement processes.
Real time Examples of AI utilization
Bridgestone Tire has implemented AI-driven predictive maintenance in its production facilities. By analyzing data from sensors on production machinery, Bridgestone can predict maintenance needs and optimize equipment uptime. This has reduced downtime, increased production efficiency, and significant cost savings.
Michelin employs advanced machine vision systems powered by AI to inspect tires for defects and anomalies. These systems detect even the slightest imperfections, ensuring the highest quality standards. Michelin’s commitment to quality control has improved customer satisfaction and brand reputation.
Continental Tires utilizes AI to optimize rubber compound formulations. Continental tailors rubber blends for specific tire models by analyzing rubber properties and performance data. This customization enhances tire performance while reducing material waste and supporting sustainable manufacturing practices.
Goodyear uses AI to design innovative tire tread patterns that optimize performance characteristics. By simulating tire-road interactions and analyzing data, the outcome is fuel-efficient, durable, and advanced tread without investing much time in research.
AI Technologies in Tire Manufacturing
- Machine learning algorithms are trained to analyze vast amounts of data from tire inspections. These models can recognize subtle defects, inconsistencies, or deviations from quality standards during production. The quality improves consistently as the machine inspects more and more tires.
- Machine learning is employed to develop predictive maintenance algorithms. These algorithms analyze data through sensors and forecast when maintenance is needed. Manufacturers can reduce downtime and lower maintenance costs by detecting potential issues early.
- Computer vision systems use AI to recognize patterns and features in tire images. High-resolution cameras capture tire details, and AI algorithms inspect these images for defects, tread patterns, and structural faults. Continental Tire is the first one to bring this technology into their facility.
- AI-driven systems can identify defects like bubbles, cracks, or irregularities during tire production. This automation enhances quality control while increasing production efficiency.
- AI robots can transport materials, assist in tire assembly, and perform tasks that require precision and efficiency. They contribute to streamlining the manufacturing process and reducing manual labor.
- The robotic arms handle tire components, ensuring precise and consistent assembly. AI-driven robotics improve tire uniformity and reduce production errors.
- Big data analytics platforms process vast amounts of data from various stages of tire manufacturing. AI algorithms analyze this data to identify trends, inefficiencies, and areas for optimization. Manufacturers can make data-driven decisions to enhance production processes and resource allocation.
- Real-time data processing is critical for quality assurance. Sensors collect data during tire production, and AI systems monitor this data in real-time. Any deviations trigger immediate alerts, allowing for quick corrective actions and no chance of human errors.
Challenges and Considerations
Initial Investment and Integration Costs
Even though the concept of integrating AI in tire manufacturing seems quite simple, in reality, it is not. First, the capital required for integrating AI machinery, sensors, and software will be much higher than expected, as only a few big companies are offering these services for now.
With that, the return on investment will also be deducted for a few years. Still, there is no predictability of whether it will be a long-term investment as new and more advanced technologies are dropping in the artificial intelligence field.
From a capital point of view, it might be hard for manufacturers to integrate AI in their factories because it might be too early for them. Also, integrating AI systems and operating them is more complex, and only specialized labor will be able to run them, and manufacturers might face scarcity in finding them.
Data Privacy and Security Concerns
In online fields, data is the most important thing, sometimes more important than profit, so if tire manufacturers hire some AI integration firms, there are chances that they might put themselves in a situation where their data is not safe anymore from their competitors.
With that, AI-integrated software and sensors can get hacked, and again, a manufacturer might lose customer data, their manufacturing techniques, and other vital data that they would not share with anyone other than top management.
So, with AI integration, manufacturers should make up their minds that the safety of their data is not the same as before or implement high-quality privacy and security features on their AI software.
Workforce Training and Skills Development
Integrating AI technologies requires a skilled workforce capable of operating and maintaining AI systems. Manufacturers may face a skills gap and must invest in training and upskilling employees.
Employees may resist AI integration due to fear of job displacement or changes in job roles. Effective change management strategies are essential to address these concerns and ensure a smooth transition.
With these, there are many more challenges that tire brands might face, including the adoption of advanced technologies, the production of high-value products, leveraging knowledge and AI systems, sustainable manufacturing, flexible enterprise capabilities, innovation, collaboration between industry and research, and new management paradigms.
Whichever manufacturer will come up with these challenges will enjoy the most benefits and advantages that AI technology provides.
Benefits of AI in Tire Manufacturing
Cimcorp, an automation company, has worked with big tire manufacturers like Michelin, Goodyear, and many others. According to them, the most significant advantage of AI automation in manufacturing facilities is that it reduces prediction costs immensely.
The same prediction cost of $250000 before AI has been reduced to $12.75 due to the lack of tests and other relative costs.
With that, there are many other benefits that tire manufacturing brands are enjoying after the automation of AI in their facility, including:
- The component requirement has reduced with the raw material requirement as well.
- Employing Modular structures optimizes the utilization of capacity.
- Predictive flexibility is possible that allows production changes.
- Maximum overall equipment effectiveness (OEE).
- Reduced power consumption.
- Effective management and control of all production parameters.
The role of AI in tire manufacturing can be seen in the practical and low-cost production that some tire brands enjoy. With time, more industries and manufacturers are adapting toward artificial intelligence due to its effectiveness and productivity with low cost and high-quality standards.
AI has totally changed the perspective of manufacturers worldwide, and tire manufacturers can save a lot just by integrating AI software and sensors.
With time, AI will completely take over the manufacturing industry, and at this rate, due to the quick innovations and advancement, the time is not so far away. So, if you are running a facility or factory of tire manufacturing, adapting to AI early will give you an edge over your competitors in the future.