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Before specifying expensive tool alloys with higher thermal conductivity, it is important to have a clear understanding of their cost- benefit ratio. That was the situation facing packaging molder Proplas S.A. in Medellin, Colombia. It wanted to reduce the cycle time of a PP part that required assembly with a disc on top to establish the closing function. Initial attempts to reduce cycle time caused the part to shrink below the specified dimension, so that assembly was no longer possible.

Proplas then reached out to Sigma Engineering GmbH in Germany (SIGMASOFT Virtual Molding is in Schaumburg, Ill.) to find an alternative way of cutting the cycle time. The mold was analyzed using Sigmasoft Virtual Molding software, which “works like a virtual
injection machine,” according to Sigma. The complete mold, with all its components, is included in the simulation, and then several virtual molding cycles are “run” one after the other, to simulate how the mold thermal profile is established during startup.

This procedure identified a hot spot in the mold at the site of critical part dimensions. The hot spot caused greater shrinkage precisely at the part location essential to the assembly function. “The heat had to be removed from that precise location, but the ejector design did not allow for the cooling line to reach that deep into the mold core,” explains Dr. Laura Florez, the Sigma engineer in charge of the project. “It was thus proposed to change the alloy in the mold core to dissipate heat faster,” and thereby reduce both cycle time and part shrinkage.

The steel in the core was replaced in the CAD model of the tool with a copper-beryllium alloy insert having higher thermal conductivity. Virtual Molding simulation showed a reduction in residual heat in the critical location, and the peak temperature dropped from 76 C/169 F to 49 C/120 F. Necessary cooling time in the critical location of the part dropped from 4.7 sec to 3.2 sec, achieving a total cycle-time reduction of 28%, and part dimensional stability was improved. This analysis enabled Proplas to determine the cost-effectiveness of the expensive tool alloy and to optimize the mold without costly and time-consuming trial and error.