Detecting periodic fluctuations in hot runners requires a multi-step approach: temperature fluctuations are identified through real-time monitoring with multi-point thermocouples and spectral analysis; pressure fluctuations are captured using cavity pressure sensors combined with SPC control charts; structural fluctuations are diagnosed through flow balance simulation and gate mark comparison. All three require the use of time-series data and methods such as Fourier transform to identify periodic characteristics at a fixed frequency.
I. Temperature Fluctuation Detection Methods
1. Real-time Monitoring with Multi-point Thermocouples: Install high-response K-type thermocouples at key locations in the hot runner, such as the main flow channel, branch flow channels, and nozzle tips, with a sampling frequency of at least 1Hz.
Record the temperature curve for each run and observe for periodic overshoot or oscillations (e.g., repeating every 3-5 runs).
Reference Standard: High-end applications require temperature fluctuations to be controlled within ±3℃.
Temperature Control System Data Analysis: Retrieve historical data from the temperature controller and check if the PID output exhibits periodic adjustments (e.g., frequent alternation between heating and cooling).
Fourier Transform (FFT) method: Perform spectral analysis on temperature data to identify the dominant frequency and determine if it is synchronized with the mold opening and closing cycle.
Dual-sensor comparison method: Set up two independent sensors in the same temperature control zone. If there is a phase difference or amplitude difference in the readings, it indicates temperature lag or improper installation.
Note: Periodic temperature fluctuations often exhibit a "sine wave" pattern, frequently caused by PID parameter mismatch or external thermal disturbances.
II. Pressure Fluctuation Detection Methods:
1.Mold cavity pressure sensor monitoring: Install piezoelectric pressure sensors at the inlet of each cavity to collect pressure peak values and curve shapes during the filling stage in real time.
If the pressure peak values are found to alternate periodically (e.g., high on odd numbers, low on even numbers), it indicates flow imbalance.
2. SPC control chart analysis: Import parameters such as maximum injection pressure, holding pressure peak, and filling time for each mold cycle into the Statistical Process Control (SPC) system.
3. Pressure Pulse Test Simulation: During the molding trial phase, pressure pulse tests are conducted to simulate periodic pressure changes under actual operating conditions and evaluate the system's fatigue resistance.
Use sinusoidal or square wave pressure, with a frequency of 0.5~5Hz, and observe whether the pressure transmission is stable.
Key Indicators: The pressure cycle frequency is typically 0.1~10Hz. Hydraulic oil or water can be used as the medium to simulate real flow impact.
III. Detection Methods for Structural-Related Fluctuations:
1. Flow Balance Simulation (Moldflow Analysis): Use Moldflow or Moldex3D software to perform CAE simulation of the hot runner system and predict the filling time differences between cavities.
If the simulation shows a filling time difference exceeding 5%, it indicates a risk of structural design imbalance. 2. Gate Mark Comparison Method: Sampling 10-20 mold samples continuously, observe whether the shear marks, weld lines, and gloss changes at the gate exhibit periodic differences.
By combining the mold batch number with the fluctuation period, determine whether it is synchronized with mold actions (such as ejection and cooling).
3. Structural Resonance Detection: Use vibration sensors to monitor the mechanical vibration frequency of the mold during operation, avoiding resonance between the hot runner structure and the injection molding machine's motor frequency.
Pay special attention to whether the impact frequency at the moment of mold opening and closing is consistent with the temperature/pressure fluctuation frequency.
