Machine condition monitoring is a practice whereby the machine's health over a specific period can be assessed. By capturing data from the machine, efficiency, overall equipment effectiveness, and other variables can be used to determine future performance to optimize parts replacement, wear and tear, and downtime for service.
But what about tool monitoring? While machine condition monitoring leverages electronically captured data, the tools themselves are precise, hardened tools that perform the cutting, boring, or milling of a piece. By extending the concept of machine condition monitoring to include tool monitoring, companies can predict tool failure and reduce cost.
Tool monitoring is more difficult to perform than machine condition monitoring. It attempts to leverage the data from the CNC machine to understand and predict the condition and lifecycle of the tool. The more precise the prediction of tool failure, the lower the overall tool cost incurred.
By monitoring various factors, tool life monitoring software can inform operators of tool wear and expected tool life. This offers greater visibility into the process and empowers operators to get as much life as possible from each tool. They can also manage their time better to perform changes with less downtime. Predicting the tool failure allows operators to replace the tool at an optimal point, while avoiding damage to the machine and part being produced, lowering scrap and tool costs.
For companies implementing tool monitoring systems, there are essentially three levels of approach. Each offers a varying degree of certainty as to indicating when a tool failure will occur, and as a result, each step offers varying degrees of effectiveness.
The first type of tool monitoring is the most basic, and it is widely used across the machining industry. It is simply a variation on an old reactive maintenance practice where the tool is allowed to run to failure. This method uses the failure points to create an average used as a replacement guide. Approximately 95% of the industry is doing this.
While this method is easy to implement (resulting in its industry-wide use), it does have significant drawbacks.
The setpoint for the average can become arbitrary or subjective, especially when different materials are used in the machine. The lowered averages then drive up tool costs.
Furthermore, there can be all sorts of other variables that are not accounted for that interrupt the work of the tool, causing it to stray from the average. This results in one of two things:
An image of a "good" part (top) and a "bad part" (second from top). Below these are two endmills: the first is new, while the bottom is broken. For this manufacturer, when the endmill breaks (like the bottom one), the slot does not get cut and the part becomes scrap.
The second level of tool monitoring is more advanced. This system analyzes the power being used by the spindle and tracks the waveform, like the sound waves on a recording, to predict failure points. By reading the amplitude of the power over time, increases and decreases in the load can indicate the failure point.
This system, too, has drawbacks. While addressing the issue raised by subjective averages in Level 1, Level 2 still can only capture tool conditions at or very near the failure point. This means that the system is still open to potentially higher scrap rates. It also means added downtime and potential damage to the machine depending on the type of failure.
The most advanced solution available, this approach to tool monitoring utilizes algorithms that can detect an approach to failure in time to replace the part within an acceptable maintenance stop and with significantly reduced scrap.
Level 3 tool monitoring systems, such as those pioneered by MachineMetrics, use high-frequency data and advanced algorithms to diagnose, predict, and avoid failures.
These systems also require no sensors to monitor the tool. By measuring high-frequency data from torque usage, data can be parsed, cleaned, and analyzed. This data is pulled directly from the machine control, thus optimizing tool monitoring to cover the last mile and becoming an integral part of a more advanced machine condition monitoring system.
There are many benefits to a tool monitoring system, including:
With tool monitoring, you can replicate the ear of an advanced machinist with years of experience listening to tools and machines for any signs of impending failure.
When BC Machining sought help to address continuing tool breakage and high scrap rates, they engaged MachineMetrics for a solution. BC Machining serves the medical, defense, transportation, and power tool industries where precision is critical.
BC Machining had been experiencing excessive tool breakage in their Swiss CNC machines, creating scrap both at the point of breakage and near-end-of-tool-life when parts can vary out of spec.
Using the MachineMetrics solution of capturing high-frequency data and analyzing it through advanced algorithms, BC was able to identify tool breakages to prevent scrap parts.
The drop in lost parts, sorting, and uncertainty translated into near 100% failure detection and a $72,000 annual savings per machine.
MachineMetrics offers a tool monitoring system that collects data directly from the source – the CNC machine itself. Through custom algorithms designed to monitor torque usage, inputs can be entered into the system as time-series events or machine learning models to predict tool failure accurately.
MachineMetrics high frequency data adapter can detect the problems and analyze the data at the edge to automate solutions and alert staff to issues before the failure occurs, preventing costly scrap and downtime. With advanced machine diagnostics, tooling is optimized and incorporated into an aggressive and fully automated predictive maintenance system. To see how MachineMetrics can be deployed for your tool monitoring needs, book a demo with our team today.
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