CNC Use Case
Spindle Wear Detection by Power Consumption Analysis
Detect spindle wear and drive inefficiencies before they cause failures — using only existing electrical data from SCADA/machine controllers. No additional sensors required.
Data Input
Analytics Findings
AI-ranked root causes of spindle power variation with explanation and recommended action
High reactive power or poor power factor indicates the drive is working harder than necessary to maintain magnetic fields rather than performing useful work.
Prioritize spindle drive vector tuning, verify motor nameplate parameters, and check harmonic filter health.
Unnecessary torque peaks drive up current demand, causing accelerated wear and excess energy consumption during cutting cycles.
Optimize feed/speed rates and enable spindle load-based adaptive feed.
Background electrical stress from inefficiently managed auxiliary systems can inflate the spindle power recorded under identical cutting conditions.
Verify coolant and hydraulic pump control modes (VFD vs. constant speed).
Poor plant power quality creates background electrical noise and stress that the spindle drive must overcome, which increases the total real power (kW) consumed.
Check plant voltage balance and grounding.
Sources: Feature importance values (kVA_spindle, PF_spindle, I_avg_spindle) derived from AI model trained on spindle electrical data. Methodology aligned with IEC 60034-30 motor efficiency standards and IEEE 519 power quality guidelines.
Expected Results
Spindle Power Consumption
10–20% reduction
Lower energy cost per machined part
Unplanned Spindle Downtime
−15–25%
Early wear detection before catastrophic failure
Tool & Bearing Lifespan
+15–30%
Reduced tooling costs and changeover time
Part Quality / Scrap Rate
−5–12%
Fewer out-of-tolerance parts from spindle runout
Acting on spindle drive efficiency alone can recover 10–20% of spindle energy — without any hardware change, using data the machine already logs.
Monitored KPIs
Business Outcome
CNC spindle failures typically cost $15,000–$80,000 per incident in repairs and lost production. By catching drive inefficiency and bearing wear early — through power consumption patterns — manufacturers prevent failures, extend spindle life, and reduce energy waste, all without adding sensors or ripping out existing systems.
Sources: CNC spindle failure cost estimates based on industry data. Motor City Spindle Repair – The True Cost of Spindle Failure; Modern Machine Shop – Fending Off Spindle Failure.
Data Privacy: Built for the Shop Floor
On-Premises / Edge Deployment
All inference runs locally. No production data leaves the facility.
No Cloud Dependency
Fully air-gapped operation possible. Analytics continue even without internet.
Zero Data Sharing
Spindle, process, and quality data never transmitted externally.
Works with Existing Systems
Connects directly to SCADA/machine controller. No new infrastructure required.
Your machine data stays on your machines. Full data sovereignty with AI-powered spindle health monitoring.
"The data to predict spindle failure already exists.
We just make it speak."