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Predictive and scheduled maintenance
How can machine downtime be prevented in automatic cutting systems?
In cutting rooms, an unexpected machine stoppage is not limited to the interruption itself: it halts ongoing orders and causes costs to rise across the entire production line.
Preventing it requires more than a single strategy. The most effective approach combines two methods: scheduled maintenance, based on the machine’s operating hours, and predictive maintenance, which uses sensors to detect the early signs of a problem. It is the same principle applied in aviation, where nothing is left to chance.
Why reactive maintenance alone costs more
Reactive maintenance comes into play when something has already broken down or when cutting quality has visibly deteriorated. However, the cost goes beyond machine downtime: it also includes the waste generated in the preceding hours, when the machine was already operating outside specifications, as well as the cost of an urgent technical intervention. Combined, these expenses almost always exceed those of maintenance planned in advance.
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The FKgroup approach: predictive and scheduled
FKgroup cutting machines follow a hybrid model that can be described as predictive and scheduled.
The foundation remains scheduled maintenance: today, approximately 90% of our maintenance planning is based on the counter that records the machine’s actual operating hours. Sensors are also used, but in a targeted way, focusing on the parameters that truly matter.
The operating-hour kit: the foundation of scheduled maintenance
At predefined intervals—for example, a 500-hour kit followed by a 1,000-hour kit—wear-prone components are replaced before they reach the end of their service life. It follows the same principle as servicing a car every 30,000 km: a planned intervention that prevents failure rather than reacting to it.
The sensors that anticipate unexpected issues: the predictive component
This foundation is complemented by a number of targeted sensors, particularly those monitoring feed force and temperature. These provide the predictive component: if a value deviates from the normal range before the scheduled maintenance interval, the system flags it so that action can be taken immediately. To return to the car analogy, servicing may be due at 30,000 km, but if the oil warning light or temperature warning light comes on first, you still stop the vehicle.
The FKgroup machine works in the same way.
The tangible benefits for the cutting room
It is precisely this combination that delivers the best results. A maintenance kit scheduled according to actual operating hours, combined with monitoring of temperature and feed force, makes it possible to:
- Reduce unexpected downtime by replacing wear-prone components before they fail.
- Plan maintenance based on actual operating hours, without interrupting production at the wrong time.
- Maintain consistent cutting quality by intervening before the machine starts operating outside tolerance.
- Make maintenance costs more predictable over time.
There is also a less obvious benefit: reduced material waste. A machine that is beginning to deteriorate may already produce out-of-tolerance parts in the hours leading up to a failure. Acting at the right time means avoiding the unnecessary disposal of material that would otherwise end up as waste, without anyone making the connection between the two.
Would you like to optimise maintenance in your cutting room?
Our team can help you define the most suitable maintenance plan for your machines by combining maintenance kits scheduled according to operating hours with the monitoring of critical parameters. Contact us to ensure greater production continuity and prevent avoidable machine downtime.
FAQs on predictive and scheduled maintenance
in automated cutting systems
FKgroup automatic cutting systems follow a predictive-scheduled maintenance model: the foundation is scheduled maintenance based on actual working hours, which today covers roughly 90% of interventions thanks to the machine’s counter, backed by targeted sensors that anticipate problems. The sensors focus only on the parameters that truly matter, keeping planning simple and costs predictable.
Reactive maintenance costs more because the bill does not stop at the downtime: it also includes the scrap built up while the machine was already running out of spec and the emergency service call. Added together, these costs almost always exceed those of FKgroup maintenance planned in advance. The gap widens in continuous, high-volume departments, such as cutting for the automotive sector, where every stoppage halts the whole line.
FKgroup cutting machines carry targeted sensors on feed effort and temperature: when a value drifts from the norm before the scheduled deadline, the system flags it and action follows at once, before the issue becomes a failure. It is the same rigour applied in the aerospace sector, where nothing is left to chance: the scheduled service stays the reference, yet a crossed threshold stops the machine anyway.
Cutting quality stays constant because FKgroup machines replace wear components before end of life, with kits tied to working hours (for example at 500 and 1,000 hours), while sensors catch any drift ahead of the schedule. This is decisive in high-precision work such as cutting composite materials, where even a slight step out of tolerance produces pieces to scrap.
Scheduled maintenance reduces scrap because a machine starting to degrade produces out-of-tolerance pieces in the very hours before the failure: acting at the right moment avoids throwing away material that would otherwise end up as scrap. The benefit weighs most where material cost is high, such as cutting for fashion, and it makes FKgroup maintenance costs more predictable over time.
