MACHINE VISION & INSPECTION “ The challenge is to find the right balance between 100% manual and 100% automatic inspection” Alain Klein, global account manager, Sick SECOND JOB Using machine vision to increase the longevity of tires – inside the factory and on the road Machine vision is an essential part of the retreading process, to ensure a tire does not have any major defects that would make it ineligible for retreading. This is not a new application for advanced vision systems, but Sick’s Alain Klein proposes that tire manufacturers could use the technology outside of the retreading factory. “Manufacturers are trying to differentiate themselves from their competitors, not only with the tire itself but also the services they are able to provide their customers,” he explains. This is especially true of fleet customers. Machine vision can be employed in the use phase of a tire to monitor wear and ensure tires are sent for retreading at the right time. Although the service would be used to promote a manufacturer’s retreading services, machine vision in the use phase can improve tire life. If a tire is identified to be wearing unevenly it can, for example, be changed to a different position on the vehicle or have its inflation adjusted. This can result in cost saving for operators by making their vehicles as fuel efficient as possible. It’s also an important safety check to ensure no tire is close to experiencing a major failure. “This is a new possibility where cameras could be used to generate revenue for the tire manufacturers – not with tires, but with information,” says Klein. It creates an opportunity for tire manufacturers to add value for their customers and form a new business model. Data collection during tire life will also support the tire maker in identifying common faults or issues that can be prevented with adequate maintenance or by redesigning an aspect of the tire. Greater visibility in this stage will mean more tires can benefit from a second or even third life. This has a direct impact on tire makers’ Scope 3 emissions, reducing the environmental impact of their tires during use. Sick, which makes cameras such as the Ruler3000 (right), says that tire life could be improved by using machine vision to monitor wear in the use phase Continental is also planning to increase automation in its tire manufacturing facilities. “Mandatory inspections include checking of tire uniformity and geometry with the help of parameters such as radial and lateral force variation, radial and lateral runout, conicity, ply steer and sidewall bulge as well as indentations,” says Howat. “Continental is running several development activities to continuously automate all these inspection processes.” The limitation in this approach is the financial viability of introducing more sophisticated sensing systems and software to interpret this data in the pursuit of automation. As Klein argues, “Everything is possible; it’s a matter of cost. The challenge is for the tire industry to find the right balance between 100% manual inspection and 100% automatic inspection.” In reality, Klein sees the implementation of complete automation as too expensive. This means there’s likely to be a middling point where tire makers are willing to invest in additional machine vision and sensors to enable automation without affecting the cost of the product. As hardware and software continue to improve, machine vision becomes even more key to enable tire makers to optimize the production process. The industry is trending toward greater use of machine vision and better data interpretation to realize the potential of this highly detailed information. Ensuring the security of this data is paramount and something that tire makers are concerned about. “They are reluctant to bring image data to the cloud but maybe they will have to due to the amount of data they need to analyze,” Klein says. Regardless of the way it’s implemented, machine vision will certainly take on an increasingly important role in tire manufacturing, especially in conjunction with emerging technologies such as AI and machine learning. 54 www.tiretechnologyinternational.com March 2024