Setting up and debugging a dynamic temperature compensation system for hot runners requires following a five-step process: hardware configuration → parameter initialization → dynamic calibration → AI model deployment → stability verification. This ensures temperature control accuracy within ±0.3℃ in the high-temperature and high-humidity environment of Wuhan.
1. Hardware Installation and Wiring Inspection
Sensor Installation: Use K-type thermocouples with a diameter ≤1.5mm, ensuring full contact between the tip and the molten metal to avoid increased thermal resistance due to air gaps.
Heater Connection: Use armored cables for connection to prevent short circuits caused by insulation aging at high temperatures; polyimide (PI) high-temperature insulation material is recommended.
Protective Measures: Install a nitrogen purging module or a nano-coating inside the temperature control box to prevent moisture intrusion and signal drift.
2. Controller Initial Parameter Settings
Table Parameter Item Recommended Initial Value Description
Sampling Period 250ms Balances response speed and system load
PID Initial Parameters P=8.0, I=1.2, D=0.3 Applicable to most PP/PC materials
Heating Output Limit 0~100% Prevents overshoot during cold start
Compensation Value Upper Limit ± 15℃ Avoids overheating caused by abnormal AI output
Operation Recommendation: Execute the "Zero-point Calibration" program after initial power-on to eliminate sensor cold junction compensation error.
3. Dynamic Response Calibration (Key Step)
Step Test Method for Obtaining Time Constant
T: Step the heating power from 50% to 70% and record the temperature rise curve;
Calculate the time required to reach a 63.2% temperature rise, which is the sensor time constant T (typical value: 1.0~2.5 seconds).
Differential Term Filtering Settings: Add a first-order low-pass filter to dTdt, with a cutoff frequency set to 5Hz to prevent noise amplification.
Feedforward Gain Tuning:
Based on the screw speed change rate, set the feedforward coefficient to 0.8~1.5 times the power pre-output to suppress heat load fluctuations during the injection stage.
Practical Value: A medical company reduced the overshoot during start-up and shutdown from ±3.2℃ to ±0.6℃ through precise calibration.
4. AI Compensation Model Deployment and Debugging
LSTM Model Input Variables: 8-dimensional data including temperature sequence, heating current, screw position, and back pressure from the past 10 cycles.
Inference Delay Control: Deployed to an edge controller (e.g., Siemens S7-1500) to ensure inference time < 10ms.
Safety Limiting Mechanism: Set the compensation output change rate ≤ 2℃/s to prevent drastic fluctuations from damaging the heating element.
Engineering Advantages: The AI model can automatically adapt to material changes (e.g., PC → PP) without manual parameter readjustment.
5. System Stability Verification and Optimization
Continuous Operation Test: Continuously produce 100 batches, recording the standard deviation of temperature at each measuring point, with a target of ≤ ±0.5℃.
Material Change Recovery Test: Switch from high-temperature material (PC) to low-temperature material (PP), observing the temperature recovery to steady state time, ideally < 3 minutes.
Fault Switching Verification: Simulate AI communication interruption, confirming that the system automatically switches to PID mode within 0.5 seconds, ensuring uninterrupted production.
Final Goal: In the production of aerospace medical components, control the contour deviation within ±0.002mm, increasing the pass rate to 99.8%.
