Canadian Plastics

StackTeck establishes iMFLUX Centre of Excellence

Canadian Plastics   

Canadian Plastics Materials Moldmaking

The Ontario-based tooling maker is now supporting the iMFLUX low-pressure injection molding process to help customers meet the challenges of molding recycled materials.

A StackTeck test machine equipped with an iMFLUX control unit. Photo Credit: StackTeck Systems Ltd.

In a move to help its customers overcome the challenges of molding recycled materials, Brampton, Ont.-based injection mold tooling maker StackTeck Systems Ltd. is introducing services to support the iMFLUX process.

StackTeck has embraced the patented iMFLUX technology as a process control method capable of handling large and sudden material property variations in real time. After qualifying a range of PCR resin grades using iMFLUX, StackTeck has received permission from Procter & Gamble to use and share this know-how that can be applied to any iMFLUX-capable injection molding machine.

“We are taking a long-term view on this initiative,” said StackTeck president and CEO Vince Travaglini. “We have been dealing with PCR resins for specialty products like flowerpots, paint cans, and rPET preforms for water bottles for many years. However, today we see a much broader demand for it. We see this as a strategically important step towards helping our customers move to more sustainable products.”

The iMFLUX process is a patented Procter & Gamble technology that utilizes a closed loop control system to manage injection pressure during every molding cycle based on real time measurement of the molten plastic in front of the screw in an injection molding machine.  Pressure measurements are taken at a frequency of 1,000 hertz, and the control system manages the system to maintain a constant injection pressure, regardless of the potential for pressure variations  that are caused by material property changes over time.


Hamilton, Ohio-based iMFLUX is a wholly owned subsidiary of Procter & Gamble Co. Its processing software integrates machine learning to allow equipment to run more reliably with fewer operator adjustments.


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