Maintenance and replacements are only carried out when really necessary. Maintenance work can be planned precisely. At the same time, unplanned downtimes and therefore downtime costs are reduced thanks to permanent measurement. To make this possible, igus has developed various sensors and monitoring modules for energy chains, cables, plain, linear and rotary table bearings with smart plastics, for example sensors for measuring abrasion or wear of the pin-bore connection of energy chains as well as sensors for breakage and tensile shear force detection. Networking with the new igus Communication Module plus (icom.plus), which igus is exhibiting at EMO, enables direct integration into the customer's IT infrastructure, for example in production management systems such as SCADA and MES or online in company-wide cloud solutions.

Flexible data integration with the new icom.plus

The icom.plus is programmed via igus online configurations with initial lifetime algorithms and can be operated offline after online installation without an update function at the customer's request. This allows the user to flexibly configure the connection of the module and thus its data and to achieve a balance between runtime maximization and IT security. With an existing online connection of icom.plus, the service life statement is continuously compared with the igus Cloud to enable maximum machine runtimes with minimum risk of failure.

Among other things, the data in the cloud draws on the data from the 10 billion test cycles of energy chains and cables carried out annually in the company's own 3,800 square meter test laboratory. Based on these tests, which are fed into the freely available online service life calculators, it is possible to predict quite accurately in advance how long an e-chain, for example, will work reliably in the respective machine tool application.

The isense components provide the customer with additional security through a permanent service life update. This is because it takes into account the current environmental conditions of the running application. Thanks to machine learning and AI, precise information on the service life of the individual solutions used in the actual running application is possible. This information can be viewed on the system control screen, and if an online connection is selected, an SMS or email also informs the user if unexpected operating conditions occur or maintenance is due. Users are informed in good time when a replacement is required; various scenarios, such as the automatic triggering of maintenance work or spare parts orders as well as "e-chain as a service", can therefore be implemented.

EMO, Hall 8, Stand E01