Hopper: GMP-compliant Parameter Optimization for Lower Scrap Rates in Injection Molding
Live on a COC syringe production line: Hopper automatically optimizes injection parameters, compensates for batch fluctuations, and increases good part output by up to 18% – without manual fine-tuning.
In our new use case video, we showcase Hopper, our self-learning optimization software, in real-life operation on a COC syringe production line for staked needle syringes.
Hopper analyzes raw material properties and high-frequency process data from the injection molding machine, detects fluctuations—such as those caused by material variations—and generates live parameter suggestions within the validated process window. These suggestions are either sent directly to the machine control system or displayed to the operator.
The result:
🔧 Up to 20% less scrap
📈 6–18% more good parts output
🧠 Self-learning and adaptive – ideal for demanding materials
Especially in complex molds or with fluctuating material quality, Hopper significantly reduces manual fine-tuning and proactively compensates for variations – delivering more stable processes even with limited skilled labor.
🎥 Watch the video now to see how AI is transforming injection molding in medical device manufacturing!
