Machine Data Collection for Deep Hole Drilling Analytics

Collecting data from deep hole drilling machines helps optimize the process and predict when maintenance is needed. Modern CNC controls already collect most of this data — it just needs to be captured and analyzed. In my experience, shops that implement even basic data collection see a 15-20% reduction in unplanned downtime within the first six months.

What Data I Collect and Why

I have settled on seven key signals that I record for every machining cycle. These give me a complete picture of process health without drowning in noise.

I store these in a SQLite database locally on a shop-floor PC. The database grows by about 2 MB per machine per month — negligible by modern standards. A single Raspberry Pi can handle logging from four machines simultaneously without breaking a sweat.

Interpreting Spindle Load Trends

Spindle load is the single most informative signal I track. A fresh gun drill typically draws 40-50% of rated spindle load. As the cutting edge wears, the load creeps up. I have documented the following pattern across three years of data:

I change tools based on these load thresholds, not on a fixed cycle count. This alone reduced my tooling costs by 18% in the first year because I stopped pulling tools that still had usable life.

One thing I have learned: spindle load trends must be viewed over a rolling window of at least 15 cycles. A single high reading could be a material hard spot, not tool wear. The trend over multiple cycles is what matters.

Coolant Pressure as an Early Warning System

The coolant pressure signal has saved me from catastrophic failures more times than I can count. I have identified four distinct pressure signatures:

• Gradual decline over 10-20 cycles: The coolant filter is loading up. I clean or replace the filter during the next shift change.
• Sudden drop of 30% or more in one cycle: A hose has burst or a fitting has failed. I stop the machine immediately.
• Slow oscillation of +/- 5% around the setpoint: The pump is cavitating, typically from a restricted inlet or low tank level.
• Steady pressure but rising coolant temperature: The heat exchanger is fouling. I schedule a flush within the week.

I log coolant pressure at 1 Hz and store the min, max, and average for each cycle. The min value catches transient drops that the average might mask.

Building the Collection System

I have used two different data collection architectures depending on the machine age and budget.

PLC-Based System (Budget ~$3,200):

• Raspberry Pi 4 with a USB-to-RS485 adapter
• PLC reads data from the CNC via Modbus TCP or MTConnect
• Data written to CSV locally, pulled into a SQL database nightly
• Total hardware: approximately $400, plus 8 hours of integration labor

Direct CNC Communication (Budget ~$5,800):

• Industrial PC with Fanuc FOCAS or Siemens SINUMERIK Integrate
• Direct register reads from the CNC for real-time data
• Data streamed to a central SQL Server database
• Full installation including licenses: approximately $5,800

I recommend starting with the PLC-based approach. You can upgrade later once you know which data signals actually drive decisions in your shop.

Key Takeaways

The single biggest insight from my data collection effort: we were changing tools 40% too often. The data proved our tools had more life than we assumed. That alone paid for the entire system in under four months.

Key Takeaways

• Collect spindle load, coolant pressure, and cycle time as a minimum viable set
• Use trend analysis over a rolling window, not single-cycle snapshots
• Start with a low-cost PLC-based system and scale up
• Tool life optimization based on load data pays for the system quickly
• Review the data weekly, not monthly, to catch developing problems early