COLOGNE, Germany—AI technologies are already are here and will gain widespread adoption in tire factories much sooner than the industry expects.
Just as smart tires can self-monitor and adapt to external driving conditions so too will tire factories "learn" to self-adjust and autonomously operate at optimum levels.
This fascinating vision of the not-too-distant future was presented by Cimcorp technology director Jyrki Anttonen at the Future Tire Conference 2018, held May 30-31 in Cologne.
Like autonomous vehicles—of which smart tires are a key component—autonomous tire plants will employ software-based connectivity, big data and Internet of Things technologies, Anttonen said.
The tire industry, he noted, already has gone from individual device level controls to integrated device controls and automated materials handling systems. Production processes have also been integrated through MES software and connection of plants into ERP systems.
"This is more-or-less where we are today," the Cimcorp director said, while also noting the growing influence of computing power, which is increasing at an exponential rate, the flexibility of Cloud computing and the availability of very large data sets.
"These have all enabled the development of things like big data and finally artificial intelligence, which enables us to make a big leap [forward] in the tire plant," said Anttonen.
AI is not a new invention.The term was first used in 1956 and AI is used today, for example, in mobile phone and online functions, without people realizing.
Indeed, the first steps have already been taken toward establishing an AI factory as seen in areas such as predictive maintenance in which sensor data is collected, stored and analyzed.
There also are machine-learning algorithms for analyzing data and learning, over time, how to predict component failures. Many suppliers already have such capabilities, which Anttonen described as "more like basic Industry 4.0 or industrial IoT functionality…on a very low component or equipment level."
Looking ahead, he foresaw the introduction of machine learning algorithms to continuously capture data on the curing-time of the green tire or analyze KPIs such as robot utilization.
Such algorithms, he added, would learn how to position SKUs to get the optimum performance from the gantry robot or any other aspect of storage within the tire factory.