Predictor Dimension Helps Solve Injection Molding Mysteries

DN Staff

April 6, 2010

2 Min Read
Predictor Dimension Helps Solve Injection Molding Mysteries

Do you think you’re ahead of the curve if you use closed loop mold pressure sensors to determine the accuracy of injection molded parts? Most design engineers and their processing partners would say yes. But a company in California says it has built a better mousetrap.

The Mold Characterization Study by Algoryx is said to “condense” all part dimensions in all cavities down to a single dimension in one cavity. That single dimension is the Predictor Dimension.

Steve Tuszynski, president of Algoryx, says that process monitoring typically involves accepting good shots and rejecting bad shots.  ”This makes it economical to do shot-by-shot monitoring in situations where it would otherwise be unfeasible. Simply, when the PD is in the Operating Range, it is a good shot. When the PD is outside of the Operating Range, it is a bad shot.”

Tuszynski further explains, “The Operating Target is the center of the Operating Range. When the PD is at the Operating Target, the parts have the highest quality (Cpk), lowest scrap and lowest reject rates.  Planned applications of this technology use a vision system that measures the value of the PD and inputs that value into a closed-loop feedback control system that automatically adjusts press settings as needed.”

Algoryx’s MCS results are independent of press settings and travel with the mold from press-to-press, region-to-region and country-to-country. This enables the processor to re-validate the mold on any tonnage equivalent press anywhere in the world.

If it sounds too good to be true, Algoryx invites you to take the Algoryx Challenge at

In one of his patents, Tuszynski states that there are at least 22 control variables in the injection molding process (a high and a low temperature, a high and a low pressure, etc.)  and more than 4 million possible combinations. “Indeed, there are billions of possible combinations when three levels (high, medium and low settings) are considered,” he says.

“Furthermore, changes to process variables may have varying effects on the resulting article characteristics; for example, increasing a pressure setting can increase a first article characteristic, decrease a second, and not affect a third. Simple interactions, complex interactions and non-linearities complicate the situation further.”

The “background” section of the patent provides a good read  on  molding complexity.

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