Thick-walled preforms are used to create containers that need to be strong, durable, and reusable. These containers are made through a 2-step stretch blow molding process. In the first step (injection process), plastic is injection molded into a preform - a compact, often cylindrical form with a threaded neck, designed for the specific container shape. In the second step (blowing process), the preform is reheated, placed in a blow mold, and expanded with high-pressure air to form the final container.
Processing heavy, thick-walled preforms into refillable containers poses challenges during both the injection and blowing processes. Thick-walled preforms are difficult to cool during the injection process and challenging to reheat during blowing.
One of the main challenges of the re-heating process of thick wall preforms is obtaining balanced temperatures across the wall thickness of the preform. If the temperature difference (delta T) between the internal and external wall is too large, it can lead to defects such as cold stretching pearlescence and heat haze.
Cold stretching pearlescence occurs when the preform is stretched at a low temperature, causing a whitish, streaky appearance due to uneven stretching of the polymer. On the other hand, heat haze results from overheating, where excessive crystallinity develops in the material, leading to a cloudy or opaque appearance in the final container. Both defects negatively impact the quality of the final product.
We are seeking technologies, such as thermal profiling, vision systems, sensors, and image recognition, to identify and measure defects in plastic containers after the blow molding process. Specifically, we are looking for a method to easily identify and measure cold stretching pearlescence vs. haze from over-heating crystallization during the blow molding process or immediately thereafter. The intention is to use this information to make real-time adjustments and corrections to the blowing process.
- An apparatus and method for the measurements.
- Imaging (e.g. infrared and polarized light cameras) or in-line optical sensors coupled with automated defect detection.
- Vision inspection systems.
- Specialized instrument providing a full thermal profile of the preform, integrated with machine learning for real-time defect prediction and process optimization.
- Identify and measure areas that have either heat haze or over-stretch pearlescence (cold stretch) around the complete perimeter of the container.
- Provide a graphic/visual feedback indicating the location of the crystallinity/pearlescence in the container.
- Deliver fast, verifiable, and repeatable results that can be used to adjust the blow molding process as it happens.
- Graphic/visual interface.
- Portability for use across different production lines.
- Ease of use, geared toward a production line environment.
- Live monitoring system capable of in-line operation at blow molding speeds (~6000 bottles per hour).
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