Calibrating a temperature equalization sensor requires three steps: zero-point calibration, master-slave comparison, and system dynamic compensation. Especially in the high-temperature and high-humidity environment of Wuhan, it is necessary to combine hardware protection and AI model optimization to ensure that the temperature measurement accuracy is stable within ±0.5℃.
1. Zero-point Calibration (Basic Calibration)
When to Perform: After replacement, before the break-in period, or during sudden environmental changes (such as the start of the rainy season);
Steps: Shut down the system and allow it to cool to below 60°C;
Purge with nitrogen for 5 minutes to remove moisture from the junction box;
Enter the HMI "Sensor Calibration" interface and select "Automatic Zero-point Calibration";
The system automatically collects the current ambient temperature as a reference and corrects the offset.
Acceptance Criterion: Zero drift < 0.3℃ after calibration.
2. Primary and Backup Temperature Difference Comparison (Static Calibration)
Data Acquisition: Primary and backup sensors operate simultaneously, continuously recording steady-state data for 24 hours;
Calibration Standards:
Average temperature difference < 0.5℃;
Number of instantaneous temperature differences > 2℃: 0;
No sustained drift (> 0.5℃/h) or burrs (> 5℃/s);
Infrared Verification: Scan the nozzle's outer surface with an infrared thermometer; deviation from the system reading < 3℃, eliminating the influence of coking.
Engineering Value: A medical company, through meticulous comparison, reduced the first-mold scrap rate after replacement from 12% to 1.8%.
3. System Dynamic Compensation (AI-level Calibration)
LSTM+Kalman Fusion Model Learning: The system refits the time constant T based on 24-hour data, updating the AI temperature control parameters;
The PHM system assigns a weight ≥ 0.8 to this point within 24 hours and incorporates it into the main control logic;
Filter Parameter Optimization: Enables a 5Hz low-pass filter to suppress signal glitches; Verifies the noise suppression effect to ensure the AI does not make incorrect compensations.
