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Systematic development starts with smart simulation

January 4, 1999

5 Min Read
Systematic development starts with smart simulation

Figure 1. As shown in this statistical model generated by a Japanese OEM for its U.S. assembly plant, front-end engineering requires less effort in the form of time and money than reactive, or back-end, approaches. APT focuses on upfront CAE analysis to reduce time and cost throughout a product's life cycle.

Innovation tends to be an overused word. But when a seasoned injection molder and tool design manager turn their attention to product development, innovative is the only way to describe what happens. Factor in some additional expertise in computer-aided design and engineering, and the results are a systematic approach to developing IM products. Who are these mysterious designers? Tom Morris and Jeff Badovick of Advanced Plastics Technologies (Ormond Beach, FL). Not surprisingly, intelligent molding simulation and analysis form the foundation of their method.

Morris and Badovick founded APT, an engineering service firm, with a commitment to take projects from concept through production using a multi-discipline, team approach. Products are engineered upfront at the concept phase, and all disciplines are involved simultaneously using CAE.

Morris tells IMM the return on investment for this systematic approach greatly exceeds that of back-end or reactive engineering. "We know from experience that time and money savings are greater," he says. "In addition, several statistical models come to the same conclusion." As further proof, Morris points to statistics generated by a Japanese OEM (Figure 1). "From the graph, it's clear that more effort is needed when planning is incomplete. That difference extends throughout the product life."

APT's crew firmly believes successful product development must take all factors of the injection molding system into account, including

  • material selection

  • mold design

  • machine class

  • coolant supply

  • feedscrew design

  • skill level and training

  • robust part design

  • quality CAD model

  • FEA validation

  • moldfilling simulation applied throughout the development cycle.

Practicing what they preach, the APT partners have helped reduce the time needed to bring products to market while boosting part quality and overall cost-effectiveness. While it may sound like the holy grail, Morris admits, these goals are attainable. Moreover, they are critical. "Without taking all system variables into account, even the best part design is of no help."

Figure 2 (left). APT begins most moldfilling simulations by generating a 3-D solid model, which must then be meshed prior to analysis. Shown is a disk package created using EDS Unigraphics, a solid modeler.

Figure 3 (at right). One of the tricks to performing accurate simulations is to import CAD data in its native format. APT uses MSC/Patran, a finite-element modeler, to bring in the disk package without the need for IGES translation. Designers then apply the finite-element mesh required for moldfilling simulation.

As an example, APT recently completed work on a revolutionary disk package, where previous jewelbox and disk package designs were thrown out the window. The OEM customer stressed basic functionality as a design criteria with an intense aim to reduce part weight and molding cycle time. Starting from a Unigraphics CAD model (Figure 2), APT focused on product design, tool design, and machine performance to help maximize the product's profit potential.

Using an MSC/Patran modeler, the CAD file was imported in its native format to create the finite-element model (Figure 3), then sent to C-Mold for future moldfilling simulations (Figure 4). The first cost-reduction came with wall thickness. APT designed an experiment to come up with the minimum wall required for the package by incrementally reducing thickness at a greater rate at thicker sections. Twelve different regions of the package were analyzed.

Figure 4. Bringing the finite-element model into C-Mold allows APT to perform a variety of part and post-fill simulations, including melt advancement, frozen layer, and clamp force predictions.

Testing the experiment's results in part-fill and post-fill simulation, designers used melt advancement predictions (Figure 5) to determine that the part could be molded with a 30 percent thinner wall than the baseline figure. Another simulation tool, the frozen layer fraction series (Figure 6, below), showed cycle times at this thickness would be reduced by 20 percent. The only increase was in the area of clamp force prediction (a C-Mold analysis output), but the requirement was still within limits set by the team. Final part designs were then verified using MSC/AFEA for structural analysis, and the optimized part design checked out.

The APT team is now turning its attention to tool design, cooling system design, FMEA, DOE optimization for processing, feedscrew design, and shrink/warp analysis using fast cool p-v-t data. Morris urges designers to use this kind of data when simulating shrink and warpage for greater accuracy. "It is a relatively new way of characterizing semicrystalline materials that C-Mold has developed and gives the analyst a more accurate representation of what is actually happening to the part," he adds.

Figure 5. After running a series of melt advancement simulations, APT found that all four proposed wall thicknesses would fill the cavity properly, including the minimum thickness at 30 percent below baseline.

Taking a similar approach to other projects has certainly helped APT rack up impressive statistics of their own. "On average, we are able to reduce cycle times by 20 percent," says Morris, "and that figure routinely goes as high as 50 percent. In many cases, we have eliminated tooling rework, the bane and time-consuming cost drain of any IM part, because the tool design incorporates all of the knowledge gained from upfront analysis. After participating in the launch of more than 200 tools, I know the benefits of being able to mold acceptable parts from the first shot."

Figure 6. Predicting when and where the melt would freeze off first in the frozen layer fraction analysis allowed APT to reduce cycle times by an estimated 20 percent.

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