Predictive Maintenance for CNC Machines
This project aims to optimize CNC machine monitoring by accurately tracking the load applied to the chuck. This monitoring improves system learning and allows for the calculation of safety thresholds. If these thresholds are exceeded, the program triggers a warning or stops the machine to prevent any risk.
To achieve this, the solution relies on a .NET DLL that connects LabVIEW to the CNC machine. This connection extracts essential data and integrates an algorithm that anticipates and prevents threshold overruns. It thus lays the foundation for a cyber-physical system (CPS) suited for Industry 4.0.
The system is based on three parallel processes: data acquisition, learning and real-time monitoring, and information logging.
Acquisition Process
The acquisition process uses a .NET DLL to connect LabVIEW to the CNC controller. The system sends regular queries and retrieves key values such as chuck load and spindle speed.
To optimize data reading, it combines all information into a single request, thereby reducing processing time. It then transmits this data to the learning, monitoring, and logging processes for accurate real-time tracking.
Learning / Monitoring
When a new tool is loaded, the system checks whether a learning phase has already been completed. If not, it automatically initiates one. It then records the spindle load over several cuts to establish a profile and analyzes the data to define safety thresholds.
Once the learning phase is complete, the program switches to monitoring mode. If the load exceeds the warning threshold, it alerts the operator without taking action. In case of a critical threshold breach, it sends a stop command to prevent any damage.
Recording
The system stores the data collected during acquisition in a log file, organizing it by the part being machined and the tool used. This structure makes it easy to access specific information and quickly generate comparative graphs. These visual analyses help identify potential irregularities or emerging trends more easily.
In conclusion, thanks to Neosoft Technologies Inc., the CNC machine monitoring application was corrected and optimized to fulfill its predictive maintenance function.
The data acquisition process through the .NET DLL was enhanced to minimize the number of requests sent to the machine’s controller. Several improvements were also made to the learning and monitoring algorithms to ensure they are stable and fully functional.
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