I. Case Study: Optimization of a 16-Cavity Husky Hot Runner System in an Automotive Precision Injection Molding Plant
Background: Previously, the total downtime for manual calibration of a single 16-channel Husky hot runner system in this plant was 3.5 hours, which could not meet the high-cycle production requirements of automotive bumpers, resulting in frequent capacity losses due to calibration.
Optimization Implementation: A multi-channel parallel sampling scheme was adopted, connecting 16 standard temperature probes at once, replacing the traditional single-channel point-by-point sampling mode.
Staggered preheating during non-production breaks was used, preheating the hot runner to 280℃, and then raising the temperature to 320℃ 30 minutes before calibration.
Historically qualified PID parameters were reused, eliminating the need for on-site recalibration.
Implementation Results: The total downtime for calibration of a single 16-channel Husky hot runner system was reduced from 3.5 hours to 45 minutes. The temperature measurement accuracy remained stably controlled within ±1℃ after calibration, resulting in a reduction of over 1.2 million RMB in downtime losses annually.
II. Case Study: Optimization of an 8-Cavity Husky Hot Runner System in a Medical Consumables Injection Molding Workshop
Background: This workshop produces precision medical consumables with extremely high temperature consistency requirements. Previously, manual recording and archiving after calibration was time-consuming and failed to meet CNAS compliance and traceability requirements.
Optimization Implementation: An automated sampling script based on a Python open-source library was deployed, directly connecting to the Husky temperature controller via PTP industrial communication to automatically and synchronously collect temperature data from the standard inspection instrument and the temperature controller.
Calibration data is directly integrated into the existing MES system, automatically generating calibration reports with CNAS traceability identifiers, eliminating the need for manual record-keeping.
A probe-free calibration method was adopted; sensors without physical damage do not need to be removed and reapplied with silicone grease, eliminating the probe disassembly and reassembly pre-processing step.
Implementation Results: The total downtime for 8-channel Husky hot runner calibration was reduced from 2 hours to 25 minutes; calibration data 100% met medical industry compliance and archiving requirements; and sensor batch consistency deviation was controlled within ±0.8℃.
III. Case Study: Batch Calibration Optimization of Multiple Husky Injection Molding Machines in an Electronic Connector Injection Molding Plant
Background: This factory has 6 Husky injection molding machines with hot runner systems. Previously, the total downtime for calibrating each machine individually exceeded 20 hours, severely impacting monthly production capacity.
Optimization Implementation: A lightweight online calibration platform based on LabVIEW Community Edition was built, connecting all hot runner channels of the 6 machines at once, enabling centralized and visual display of calibration data across the entire workshop.
Staggered calibration schedules were implemented, utilizing weekends (non-production windows) to perform calibration work on multiple machines in parallel, without affecting weekday production time.
A dynamic adjustment mechanism for sensor calibration cycles was established. For sensors with a deviation of <±0.5℃ after 3 consecutive calibrations, the calibration cycle was extended from 3 months to 6 months.
Implementation Results: The total downtime for batch calibration of the 6 Husky hot runner systems was reduced from 20 hours to 3 hours; the annual total calibration frequency decreased by 40%; and equipment uptime increased by 3.2%.
