Agytek TLMS empowers manufacturers with intelligent algorithms, and real-time analytics to monitor tool conditions, prevent failures, and optimize machining performance.
Traditional machining environments often rely on manual inspections and conservative tool replacement practices, which lead to inefficiencies and hidden costs.
a broken tool may continue cutting until catastrophic failure-damaging parts and fixtures.
due to fixed replacement schedules without real data.
that slow production and introduce errors.
due to insufficient machining data.
from tool failure or collision incidents.
due to manual setups, changeovers, and inspections limiting unmanned production potential.
Increase Cost
Lower Productivity
24/7 or Unmanned Production Impractical
Agytek TLMS integrates advanced sensing technology, edge computing, and intelligent analytics to monitor tool health and machining conditions in real time.
Continuously monitors machining conditions to detect tool wear, breakage, and abnormal signals instantly.
Uses vibration and power signal analysis with self-learning algorithms to identify tool breakage or chipping.
Continuously monitors wear patterns to optimize tool usage and extend overall tool lifespan.
Achieves detection accuracy exceeding 99% to ensure precise identification of tool conditions and prevent failures.
Leverages real-time data and analytics to identify inefficiencies, improve machining performance, and optimize overall production processes.
Agytek’s TLMS solution spans multiple integrated systems — all aimed at enabling truly unmanned CNC production by sensing, protecting, optimizing, and controlling machining operations.
Real-time tool condition monitoring eg: wear, chipping, breakage, remaining life.
Spectrum Analysis
Real Time Data
• Avoid catastrophic failures
• Reduce unnecessary replacements
Better than traditional load-counting methods
• Uses vibration and power signal analysis
• Applies self-learning algorithms for detection
• Identifies tool conditions in real time
• Triggers alerts upon abnormalities
• Automatically stops the machine to prevent damage
Tracks over 30 signal dimensions to deliver highly accurate and high-fidelity tool health evaluation.
Detects abnormal impacts in X/Y/Z axes and automatically stops machine in < 3ms.
• Avoid crashes that damage workpieces, fixtures, or machines
• Protect overall machining operations
• Ensure safer conditions for autonomous machining operations
• Support reliable and secure unmanned production
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KPI
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Typical Change
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Value Rationale
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|---|---|---|
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Tool Life Extension
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+ 30% ++
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Real-time monitoring avoids premature/tool damage replacements
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Tool-related Downtime
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↓ 40 - 70%
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Automatic detection + collision protection prevent long downtime events
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Scrap Rate
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↓ 25 - 50%
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Early detection avoids cutting bad parts for whole batches
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Cycle Time
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↓ 8 - 15%
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Adaptive machining adjusts feeds and speeds intelligently
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Operator Touch Points
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↓ ~ 70 - 90%
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Automated measurement & inspection significantly reduce manual interventions
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Unmanned Runtime
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0 → 24/7
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Retractable tools, onboard measurement & automatic stop enable night / weekend runs
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Predictive Insight
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Manual → Data-Driven
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Provides real metrics for life prediction & planning
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Strategic Impact for a CNC Plant Â
Turns CNC machines capable of running unattended – crucial for lights-out operations
• Self monitoring
• Self protecting
• Self optimizing units
Makes Costs Visible & Actionable
• Unmonitored breakage
• Unnecessary demolition
• Manual inspection
Drives smarter, more efficient operations through data and predictive insights
• Continuous performance improvement
• Real-time data visibility
• Early fault prediction