Ensure Stable Production with CNC Spindle and Tool Health Assessment System
30/12/2022
Project Introduction
Precision-machined products are expensive. If unexpected tool chipping and breakages happen during machining, it could cause quality issues and a whole batch of products may have to be rejected, meaning productivity will be affected and extra costs incurred.
Also, although the probability of component failure is relatively low, should such an event occur, delivery of spare parts could be delayed by days or even weeks and this can make the loss of production difficult to estimate. These kinds of problems are caused by a failure to detect anomalies in time, in advance.
Also, although the probability of component failure is relatively low, should such an event occur, delivery of spare parts could be delayed by days or even weeks and this can make the loss of production difficult to estimate. These kinds of problems are caused by a failure to detect anomalies in time, in advance.
System Requirements
The precision CNC machine center has become a very common system used in precision manufacturing. However, unexpected failure of a spindle or tool can cause the shutdown of a production line, damaged workpieces, as well as unstable product quality. To maintain stable and reliable precision machining, a predictive maintenance system with a Prognostic and Health Management (PHM) solution will be the key to addressing the following issues:
- The unit cost of cutting tools is relatively low, but a tool change can stop a production line for 4-6 hours. The decision for tool replacement timing is made based on the careful judgement and experience of a skilled engineer who may rely on something as simple as the level of workpiece burrs or noise a tool makes. Quality defects caused by failure to detect a tool anomaly before it has caused problems may result in products having to be called back after shipping. It is therefore crucial to monitor tool health in real time to minimize or avoid later callback costs.
- Tools wear intensively when hard-to-process materials like composites, expensive aerospace metals, or titanium alloys are machined. However, tool changes before the end of a tool’s useful life can result in high hidden costs. To avoid this, tool health should be monitored in real time to determine the optimal timing of tool replacement and minimize wasted tool life.
- If a spindle issue occurs, the machine may be down for maintenance for several days while engineers carry out measurements, repairs, and tests. Long downtimes are problematic for large-quantity orders with tight delivery times so operators need to be able to monitor spindles to detect early-stage anomalies. This will help suppliers be notified in advance of the need for spare parts as necessary.
Project Implementation
- IFS-PHM-MIC770W5A : iFactory Edge Machine Predictive Maintenance System
- iFactory PHM I.App: PHM Service Ready Platform
System Diagram
Conclusion
- PHM-based monitoring and fault diagnosis of machines offers an effective method to manage the maintenance of robot arms. Potential device failures can be accurately identified at an early stage and the machine service providers can be notified automatically. The system also makes suggestions in advance about the optimal timing for maintenance work to minimize the impact on scheduled production plans.
- When using an unsupervised learning algorithm, only healthy robot arm data is needed for the implementation of real-time fault detection. This greatly reduces the possibility of unexpected machine downtime which leads to productivity loss.
