Why Cycle Time Is More Than Just Speed
The instinctive reaction to cycle time optimization is simply raising path velocity. In practice, this often leads to increased wear, greater vibration at the tool, and in the worst case, process failures. Sustainable cycle time reduction almost always comes from smarter path planning, better use of wait times, and a clean analysis of where time is actually lost in the cell - not from simply "driving faster."
1. Path Blending Instead of Exact Stops
Every exact stop at an intermediate point (FINE on FANUC, C_DIS/C_PTP on KUKA, Zone 0 on ABB) costs acceleration and deceleration time. Wherever the process doesn't require an exact stop, blending zones should generally be used. Even a zone of a few millimeters can save several hundred milliseconds per pass - across a hundred cycles per day, that adds up significantly.
2. Minimizing Motion Sequence and Path Length
Many programs have grown organically over time and contain unnecessary detours. A systematic analysis of point sequence - similar to a simplified traveling-salesman problem - often reveals significant savings potential. This is especially worthwhile for gripping sequences with multiple pick and place positions, where reordering the approach route pays off.
3. Running I/O Operations in Parallel Instead of Sequentially
A common time loss comes from executing gripper actions and motions sequentially, even though both could happen simultaneously. Modern robot controllers allow digital outputs to be switched during motion (e.g. $OUT_C on KUKA for position-dependent switching, trigger commands on FANUC and ABB). A gripper can already be activated during approach instead of only after coming to a complete stop.
4. Optimizing Acceleration and Speed Profiles Per Axis
Not every axis needs to move at the same percentage speed. For motions dominated by a single axis (e.g. a pure wrist rotation), axis-specific speed settings often achieve more than global percentage values. Adjusting acceleration ramps (soft-start/soft-stop) can also be counterproductive when applied uniformly across the whole cell instead of selectively to critical path segments.
5. Replacing Wait Times with Predictive Logic
A static WAIT command with a fixed time value is the most common cause of unnecessarily long cycle times. Signal-based waiting (WAIT DI[x]=ON) instead of a fixed delay responds immediately once the condition is met, rather than always consuming the maximum wait time. Even better: predictive logic that triggers parallel process steps already during robot motion.
6. Simulation-Based Path Optimization Before Commissioning
Offline simulation tools (RobotStudio, Process Simulate, KUKA.Sim) allow different path variants and acceleration profiles to be tested virtually before the real system is even running. This not only saves commissioning time but also reveals collision risks that could arise from overly aggressive optimization.
7. Regular Cycle Time Audits After Production Start
Cells that were optimally tuned years ago often lose efficiency gradually due to tool wear, changed part tolerances, or subsequent program modifications. An annual audit with per-segment timing reveals where time has quietly crept back in.
Limits of Optimization: When Speed Becomes a Risk
Not every cycle time reduction makes sense. For safety-relevant motions near people (collaborative applications), mechanically stressed parts with tight tolerances, or processes with thermal inertia (e.g. adhesive application), overly aggressive optimization can compromise process quality. Cycle time should always be evaluated together with quality metrics, never in isolation.
Conclusion
The largest cycle time gains rarely come from raising maximum speed, but from smarter path planning, parallel I/O usage, and eliminating unnecessary wait times. A systematic, measurement-based approach almost always beats simply "turning up the percentage" in practice.