improve manufacturing efficiency through machine learning
With the digitization and the integration of manufactuirng processes incentivized by Industry 4.0 plan, companies began to have access to huge amounts of data (big data). In fact, different platforms are spreading that allow to acquire and historicize huge amounts of data.
What to do with this data?
There is a common feeling that there is more attention to storing them rather than how to use them. Furthermore, a massive production and process data capture, if an end in itself, could be counterproductive. Through jpiano® platform it's possible to create added value throgh this data. In particular, a supervisione module has been developed for the jpiano® platform to caupure, organize, process and make them easily usable.
This solution has been called jpiano® Embedded e makes a direct link between management applications and the physical level of machines.
Data management by jpiano® Embedded are related to manufacturing orders, machine parameters and process data.
In this way, the use of an external intelligence, which has a specific automatic learning approach that allows to identify optimization schemes or the onset of defects or criticality. In some cases it's also possible to suggest ways to solve problems already found and resolved in the past.
For example, it is very useful to consider the reports on the occurrence of possible critical issues before they take place.
In this case, it's essential to receive timely notification (and different kind of information/suggestions) and this can be easily obtained with the help of modern IoT technologies such as wearable devices (e.g. smartwatch).
Intervening in a targeted manner before inefficiencies take place during manufactuirng process is at the base of predictive maintenance
- improve the manufaturing efficiency of machinery by reducing downtime due to faults or adjustments in the operating parameters
- improve the service life of the machinery with a consequent reduction in maintenance and/or replacement costs, as it is attempted to eliminate a priori the possible causes of faults
- improve the service provided to customer as production is not slowed down by the maintenance necessary in the event of major machine failures