Tool making often requires long processing times of 48 hours or more. Some of these are also carried out on weekends. To date, production has often been carried out in shifts for the operating staff, as the machines are very complex and the processing processes require a high level of precision.
Unmanned production and automated monitoring of the production process would be a desirable scenario, especially in view of shift work and the lack of skilled workers. However, an unobserved tool breakage, for example, represents one of the greatest risks. This can lead to significant follow-up costs because the workpiece is damaged and has to be reworked. For high-priced workpieces, such as those often used in tool making, the damage could even amount to several million euros.
Solution: Autonomous production with permanent, automated process monitoring
To reduce the risk of tool breakage, companies can improve autonomous manufacturing using sensor data and artificial intelligence (AI). For this purpose, machine and additional sensor data are recorded securely 24/7, for example in CuttingEdge World, the IIoT platform for machining. The cutting tool is serialized by QR codes to uniquely identify it. The data is then merged into a digital twin that provides a complete picture of the machine, workpiece, cutting tool and process.
This digital twin can be used to detect anomalies that indicate impending tool breakage, e.g. due to the spindle performance or structure-borne noise, the changes of which can already indicate a tool breakage. Wear limits can also be determined to optimize the timing of tool changes. When an anomaly is detected or a wear limit is exceeded, an alarm is sent to employees, who can then intervene.
Example 3D representation of spindle performance in a milling process
Benefit: Reduce error rates and rejects, counteract the shortage of skilled workers
Autonomous manufacturing using sensor data and AI models offers companies numerous advantages. This includes:
Continuous remote control: Production can be monitored at any time and from anywhere. This allows anomalies to be detected and corrected at an early stage. There is no need for on-site staff or only a reduced number of staff.
Reduced risk of errors: By detecting anomalies and determining wear limits, the risk of tool breakages and other errors is reduced.
Early interception of the consequences of errors: By reporting alarms to employees, the consequences of errors can be intercepted at an early stage, which leads to cost and resource savings.
Additional benefit:
In addition to the advantages mentioned above, autonomous manufacturing using sensor data and AI also offers further opportunities to optimize processes and increase efficiency. This includes:
Process optimization (development): The data from autonomous production can be used to optimize processes. This can improve productivity and product quality.
Optimum service life of the cutting tools: By determining wear limits, the optimal service life of the cutting tools can be achieved. This reduces costs.
Fast fact-based analysis for complaints: In the event of complaints, the data from autonomous manufacturing can be used to carry out a quick and fact-based analysis. This allows the causes of complaints to be identified and resolved more quickly.
Increasing OEE (overall equipment effectiveness): By improving process reliability and the availability of the machines, the overall equipment effectiveness (OEE) can be increased.
Conclusion:
Autonomous manufacturing using sensor data and AI offers numerous advantages to tool manufacturing companies. This includes reducing the risk of errors, early detection of the consequences of errors and the possibility of optimizing processes and increasing efficiency.
