The cost of testing and maintenance for laser cutting machines is high. With an analysis of machine data, maintenance times can be better determined. Codestryke GmbH has developed an IIoT solution that enables condition monitoring and predictive maintenance.
The challenge: Inefficient maintenance and missing data
The 2D laser cutting machines from Trumpf are powerful precision processing machines. With six kilowatts of laser power, they cut steel sheets up to 25 mm in diameter and aluminum sheets up to 16 mm. Stainless steel in particular produces mirror-smooth cut edges that often no longer require reworking. Special cooling of the workpiece ensures that even unusual geometries are easy to cut.
The stress, especially in three-shift operation, means heavy wear for the machine. Nozzles are damaged, the lens can lose its focus and the mechanics of the movable cutting head can wear out. One user of the laser cutters wanted to reduce the amount of testing and maintenance required. However, there was no simple solution for analyzing malfunctions, optimizing maintenance intervals and determining data on the overall equipment effectiveness (OEE).
As a result, troubleshooting during the repair of a failed machine was very time-consuming and therefore expensive. In addition, the maintenance intervals were very inefficient due to a lack of data – wear was not uniform, and only a few problems could be corrected at the maintenance dates. Obtaining data on machine status would only be possible with “rewiring” of PLCs and gateways. The effort required for this makes this solution uneconomical. Digital technologies and the Industrial IoT (Internet of Things), on the other hand, offer a much more cost-effective solution.
The solution: Retrofit with sensors and edge device
The company collaborated for the development of a solution with the Codestryke GmbH. The provider of Industrial IoT solutions for mechanical engineering and production technology recommended a solution in form of the iCOMOX Sensor Box from Arrow. The Sensorbox is an open development platform for IIoT-based plant monitoring and is especially suitable for retrofit solutions. The compact device has various mounting adapters and is attached to the inside or outside of the machine.
The sensors of the IoT box detect vibration, magnetic field, acoustic data and temperature. Because they generate significant amounts of data, Codestryke uses an edge device for filtering and compression. It only forwards measurement changes to the cloud and thus does not overload the connection to the IoT platform in the cloud. Depending on the situation on site and the customer’s request, the transmission takes place via Narrow Band IoT (cellular), by cable(Power over Ethernet, PoE), or via SmartMesh IP wireless network.
Due to the cost-effective hardware, the client has data from the laser cutting machines available within a very short time. They are used to detect defective components and calculate overall equipment effectiveness (OEE). In addition, all data is available for further analyses, such as predictive maintenance.
The result: Predictive maintenance results in high plant availability
The solution uses an analytics application in the cloud to evaluate sensor data and determine the condition of a laser cutter’s components at any time. This makes it possible, for example, to immediately detect bearing damage when troubleshooting after a malfunction – including information about which bearing is involved.
Predictive maintenance based on plant data enables efficient maintenance schedules: There is neither unnecessary maintenance nor surprising failures. Lastly, as a by-product, the data also allows for an OEE solution that calculates all the factors needed to calculate overall equipment effectiveness. All in all, this creates a modern and cost-efficient solution for condition-based maintenance.
