Case Study: Increasing Grinding Line Output by Cutting Air‑Cutting Time and Downtime
How real‑time monitoring of a grinder motor helped a bearing plant turn air‑cutting and downtime into productive cutting time and higher output.
The operations team wanted to increase production from existing grinding assets, without adding new machines or extra shifts. However, they faced three key issues:
Challenge
A leading bearing manufacturer operates precision grinding machines at one of its plants, where each minute of effective grinding time directly impacts total bearing output. Despite having robust equipment, the plant suspected that a lot of time was being lost in “air cutting” (spindle running without material removal) and avoidable machine downtime.
Manufacturing Context
No detailed, real‑time view of how much time the grinder spent in actual cutting versus air cutting.
Limited visibility into the true extent and reasons for machine downtime across days.
No objective way to compare the actual grinding load curve with an ideal curve to identify process improvement opportunities.
They needed a way to monitor grinder behavior at asset level and convert power‑time patterns into actionable insights.
The manufacturer engaged our team to deploy an IIoT 4.0–based, asset‑level monitoring solution on a critical grinding machine, covering the main grinder motor, hydraulic power pack, and hydraulic pump motor. The solution included:
Real‑Time Asset‑Level Monitoring
Continuous power monitoring: Real‑time measurement of power consumption on the grinder motor to build a detailed time series of machine behaviour.
Automatic classification of machine states: Our analytics distinguished between active grinding, air cutting, and downtime based on power signatures.
Event and time‑series analysis tools: Daily charts and timelines helped the client correlate patterns with changeovers, maintenance activities, planning gaps, or manpower issues.
Ideal vs. actual grinding curve comparison: The platform compared actual grinding load curves with an ideal profile to highlight optimisation opportunities in infeed speed and rapid approach.
This transformed the grinder from a “black box” into a transparent, data‑rich asset.
Key Insights
Machine downtime reduction potential
Over 10 days, the machine exhibited 63+ hours (about 2.6 days) of downtime, equivalent to roughly 26% of available time.
Air‑cutting time reduction potential: Over a 7‑day period, the grinder showed 40+ hours (about 1.7 days) of air cutting, representing around 24% of the time as non‑productive spindle running.
The real‑time data revealed significant improvement potential:
Pump running unnecessarily: The hydraulic pump was often ON even when the grinder motor was OFF, indicating energy waste and avoidable wear on auxiliary equipment.
Abnormal load spikes and process risk: While the grinder motor usually consumed less than 17 kW, on at least one occasion power spiked to 25+ kW. Such spikes can be associated with excessive feed rates and risk damaging the grinding wheel surface and causing workpiece quality defects.
Micro‑optimisation opportunities: In some cycles, about 7 minutes of air cutting were observed during approach (for example, between 12:07 and 12:14), suggesting that optimising rapid approach distance and infeed speed could directly increase effective grinding time.
Adjust process parameters (e.g., infeed speed, rapid traverse distances) to bring the actual grinding load curve closer to the ideal curve, improving both productivity and quality.
Actions and Improvement
With these insights, the plant engineering and production teams could:
Set specific targets to reduce air‑cutting time across days and shifts, converting idle spindle time into productive cutting time.
Investigate high‑downtime days, correlating the time‑series patterns with ground‑level events such as changeovers, maintenance delays, or planning gaps.
Ensure hydraulic pumps are switched off when the grinder is off, reducing unnecessary energy consumption and equipment wear.
Address abnormal load spikes proactively to protect grinding wheels and avoid quality rejects.
By converting detailed grinder power‑time data into clear insights on air‑cutting time, downtime, and abnormal load spikes, the solution gave the plant a practical way to uncover hidden capacity on the grinding line. The team could see exactly when the spindle was not cutting, why the machine was idle, and where process settings or auxiliary usage needed correction to unlock more productive grinding time from the same asset.
BENEFITS OF TECH
Reduce machine downtime with asset‑level visibility on when and why the grinder is not cutting.
Increase effective grinding time by reducing air‑cutting time identified through real‑time power and time‑series analysis.
Optimize grinding parameters by comparing actual grinding load curves with ideal curves for more effective machining.
Cut energy waste and auxiliary wear by identifying when pumps and auxiliary motors are ON even when the grinder is OFF.
Protect grinding wheel and product quality by detecting abnormal power spikes that can cause surface damage and defects.