Packaging efficiency and flexibility

Beckhoff solutions, involving AI and automation, guarantee an effective management of packaging processes.

Artificial intelligence (AI) is revolutionising the automatic machines sector, among others. The integration of AI and automation into Beckhoff’s solutions - such as XTS NCT and XPlanar, thanks to the TwinCAT automation suite - represents, in fact, a radical breakthrough for the sector. It’s a system which is able to increase flexibility and the degree of customisation, as far as optimising packaging lines, making them more efficient and sustainable.

XTS NCT: flexibility without contact. The XTS NCT extended Transport System with contactless energy and data transmission is an innovative solution for packaging, particularly for its capacity to manage movements in a dynamic way, allowing for the transport and handling of products with great precision, but without the need for physical connections. This leads to greater flexibility in packaging operations, reducing format change times and optimising the production of variable batches. The integration of artificial intelligence, moreover, enables the system to automatically adapt to the needs of the production line, ensuring an efficient management of the processes.

XPlanar: free and customised movements. XPlanar is another example of how Beckhoff helps packaging. It’s a system based on magnetic levitation “tiles”, which allows for a completely free movement of the products in six directions. With XPlanar, it’s possible to customise the transport of every element on the basis of its specific characteristics, optimising the workflow and reducing the waste of materials. In addition, the movements can be programmed and modified in real time, thanks to the integration of machine-learning algorithms, making the system ideal for complex and tailored packaging operations. TwinCAT Machine Learning. Part of Beckhoff’s automation platform, the

TwinCAT Machine Learning software consists in integrating AI models directly in the packaging line control systems. This makes it possible to monitor the performance of the machines in real time, optimising parameters such as speed, pressure and temperature. In addition, through automatic learning, the system can predict and prevent breakdowns, reducing stoppage times and improving overall efficiency. In short, thanks to TwinCAT Machine Learning, companies can exploit operating data to optimise every packaging process phase. Thanks to TwinCAT, finally, XTS and XPlanar can be combined together, adding robotics, to create large-size systems with any number of movers.