While dual-sensor redundancy comparison technology offers high reliability, its implementation faces four core challenges, making it difficult for most small and medium-sized factories to adapt:
1. High requirements for installation space and coaxiality: The installation space in hot runner systems is inherently very compact. Installing two sensors of the same size side-by-side in the same location places high demands on the opening design of the hot runner template. Furthermore, it is crucial to ensure that the sensing diaphragms of both sensors are on completely identical pressure planes. A coaxiality deviation exceeding 0.1mm will lead to excessive initial output deviation, rendering the comparison base invalid. The manufacturing and installation difficulty is significantly greater than with a single sensor.
2. Extremely high requirements for initial calibration consistency: Both sensors must be calibrated in the same batch with the same precision. The initial output deviation must be controlled within 0.05%FS. Any initial deviation will be misinterpreted by the system as aging drift, leading to false alarms. If the two sensors age at different rates, the initial deviation will increase after a few months, requiring recalibration. The maintenance threshold is much higher than with a single sensor.
3. Doubled Costs, Unacceptable to Small and Medium-Sized Factories: The need to purchase an additional sensor set and add a signal acquisition channel directly doubles the hardware procurement cost. For conventional injection molding production, the increased cost far outweighs the benefits of avoiding scrap. Only high-end production with high-value single batches can cover the additional costs.
4. False Alarm Risk Difficult to Completely Avoid: Uneven installation stress or temperature fluctuations can cause temporary deviations even if both sensors are functioning correctly, triggering false alarms. Frequent false alarms can erode trust in early warning systems, potentially causing missed real anomalies and increasing production risks. This places high demands on the system's threshold settings and algorithm optimization.
These challenges determine that it is only suitable for high-end precision injection molding scenarios. Its cost-effectiveness is very low for most conventional injection molding scenarios, and it is not recommended for ordinary factories.
