The hidden foundation behind high-performance injection molding
- Plastics
- 31.7.2026
- Reading Time: {{readingTime}} min
Contents
When conventional control architectures become the bottleneck
The performance of any injection molding machine is fundamentally limited by its control architecture. Before optimizing higher-level process functions such as injection or mold movement, core system constraints must be addressed at their source.
Dead time, signal jitter, limited control bandwidth and insufficient synchronization between machine axes define the practical performance limits of conventional control systems. These limitations reduce machine responsiveness and prevent the control system from fully utilizing the machine's mechanical capabilities.
Building the foundation: Basic Control Technologies
Our Basic Control Technologies address these architectural limitations directly at the control and drive level.
The most important Basic Control Technologies and their core benefits:
| Smart Function | Core Benefit | Axis Type |
|---|---|---|
| Fast Reaction Control | One-cycle control execution | Electric & Hydraulic |
| Fast-Cut-Off Detection | Accurate cut-off detection and DO switching | Hydraulic |
| Closed-Loop Pressure Control on KeDrive D3 | 125 μs fast pressure control, reduced overshoot | Electric & Hydraulic |
| Fast Cross-Communication on EtherCAT | 125 μs synchronization between axes | Electric & Hydraulic |
| Torque Follower Control on KeDrive D3 | 125 μs torque distribution across axes, drive protection | Electric |
| Position Balance Control on KeDrive D3 | 125 μs multi-axis position synchronization | Electric |
| Cut-Off Detection on KeDrive D3 | 125 μs cut-off detection and response | Electric |
Sounds interesting? Discover how our Basic Control Technologies address key control challenges in our latest white paper. Learn how they work, explore the engineering principles behind them, review detailed system architectures, and see measurement results from real injection molding machines.
A white paper sneak peek: How Closed-Loop Pressure Control on KeDrive D3 enables a 12× shorter control response
Compared to conventional PLC-based architectures, drive-level execution with KeDrive D3 achieves cycle times as low as 125 μs instead of approximately 1.5 ms.
Let's take a closer look at where this comes from. In conventional closed-loop control systems based on a Programmable Logic Controller (PLC):
Sensor read >> Calculation >> EtherCAT transfer >> Drive output
This typically requires three cycle ticks (~ 1.5 ms in conventional systems). The resulting delay primarily impacts part quality, as it leads to pressure overshoot or undershoot. In addition, it can cause oscillations and instability during pressure release.
These effects translate directly into several critical implications for the injection molding process. Want to know what they are? Download the mentioned white paper to learn more.
“12× shorter control response with cycle time 125 μs instead of 1.5 ms.“
From individual technologies to one coordinated control system
While each Basic Control Technology addresses a specific architectural limitation, their combined operation provides the following system-level capabilities:
- Minimal control dead time
- High closed-loop bandwidth
- Deterministic communication
- Reduced pressure overshoot and minimized oscillations
- Stable torque and pressure dynamics
- Reliable multi-axis synchronization
- Higher drive utilization efficiency
Overall benefits at a glance
These capabilities translate directly into measurable process benefits:
- Faster and more precise process control
- Reduced mechanical stress
- Higher repeatability
- Improved energy efficiency
Conclusion: Foundation for faster, more precise and highly synchronized injection molding
Together, these technologies transform the machine from a delayed-response control system into a high-speed, deterministic control architecture capable of operating at the physical limits of modern injection molding equipment.
Discover the full technical details on individual functions and measurement results in the white paper.
The images in this article were edited using AI.
