AB - This paper addresses a production scheduling problem in an injection molding facility. Overview Fingerprint. Abstract This paper addresses a production scheduling problem in an injection molding facility. Keywords Changeover cost Multiple capacitated resources Scheduling Sequence-dependent setup.
Access to Document Link to publication in Scopus. Link to citation list in Scopus. Fingerprint Dive into the research topics of 'Scheduling injection molding operations with multiple resource constraints and sequence dependent setup times and costs'. Together they form a unique fingerprint. The new design has two objectives; reduce weld-line severity and reduce required injection pressure, but with the same variables—moving the gates.
One can see from the initial results that these might be conflicting goals; gates closer together make for a less visible weld line but gates farther apart require lower injection pressure. The goal is to find the best compromise between the two. The new objective is defined and the existing results from the previous simulations variants are reorganized with both objectives in mind Fig. Weld lines are still visible, however. Further reduction in melt pressure is available via gate and runner diameter changes and would be the next step in this new approach.
A gate and runner optimization would be coupled to a viscosity-curve study where the gate and runner diameters would be coupled to different filling speeds to determine the limits of the operating window, thereby saving further time at the molding machine trial.
Another common thermoplastic molding challenge is cycle time specifically cooling time vs. These goals are conflicting: Shorter cooling time makes for a hotter part at mold open and likely leads to greater shrinkage outside of the mold where there is no mold to hold it in shape , which results in a distorted part.
It shrinks less outside the mold and typically has less distortion. The objective is to find a way to reduce the part temperature at ejection and also reduce the cooling time in the mold. The variables are the 11 green cooling lines moving independently Fig. The cooling lines are moving towards the hotspots in the mold in order to reduce the local mold temperature. Mold-steel inserts of three materials—Moldmax high-thermal-conductivity copper alloy materion.
The stated objective for the software is Minimize: part temperature at ejection. Once the best cooling positions are found, the stress calculations for shrinkage and warpage are computed to evaluate the difference between best and initial scenarios. The first objective is to achieve the most uniform mold-surface temperature lowest Delta T over that surface area.
To perform the optimization, an evaluation area is defined as the mold surface in contact with the melt. There are three mold materials and 11 independent cooling lines with between two and seven available positions, resulting in 3.
Using Autonomous Optimization with goal-oriented objectives, the complete design space does need to be simulated. The software learns which variables result in reaching the objectives faster.
In this case, only calculated variants eight generations of 22 designs each were required to find the optimal solution Fig. The cost of multiple automated simulation iterations is much less than the value of the time of a trained simulation software operator.
Figure 17 shows the effect of different mold materials and water-line positions. Moldmax alloy yielded the most uniform mold-surface temperature.
The hot spots in the mold reduced their temperature from F to F Figs. Since the material can be ejected as soon as it reaches F, the results show this occurs at a cooling time of 37 sec compared with the previous time of 42 sec. The stress calculations show reduced distortion at a faster cycle Fig.
The amount of labor required to manually set up, and evaluate variants is estimated at 50 hr, while Autonomous Optimization reduced this to only 4 hr. It also makes sense to focus on software development that computes more quickly. This approach is statistically proven to find better solutions. Matt Proske is the v. He is responsible for growing the awareness in North America of Sigmasoft Virtual Molding software for injection molding thermoplastics, elastomers, thermosets, and PIM materials.
Proske worked in the metals casting industry for 14 years before joining Sigma nine years ago. Contact: m. Rodrigo Gutierrez is a project engineer at Sigma Plastic Services. He worked in metal casting for 10 years and recently joined Sigma to focus on the implementation of Autonomous Optimization technologies in the injection molding industry. Gabriel Geyne is a sr. He has five years of experience in injection molding simulation using Sigmasoft Virtual Molding. When a sprue or part sticks, the result of trying to unstick it is often more scratches or undercuts, making the problem worse and the fix more costly.
The reason you dry certain plastics is to get the moisture out. But why does the moisture have to be taken out before processing? Most molders work with two parameters for establishing second-stage pressure. But within Scientific Molding there are actually four. Injection Molding.
FIG 7a Pressure in the cavity from the original design, showing jetting and air entrapment. FIG 7b Pressure in the cavity from the best gate location shows no jetting or air entrapment. FIG 18a Worst thermal gradient of all the iterations simulated, showing hot spots. FIG 19a Highest part temperature at ejection— F.
FIG 19b Highest part temperature at ejection F , after elimination of hot spots. Matt Proske, Sigma Plastic Services.
Predictions for the 3D Printing and Recycling Sectors in Mixed Pricing Outlook for Volume Resins. Here Are Some Solutions When a sprue or part sticks, the result of trying to unstick it is often more scratches or undercuts, making the problem worse and the fix more costly.
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How does injection molding simulation software help? How can Moldflow injection molding simulation software help? Predict surface finish defects. Investigate the causes of warpage, then examine options to minimize or correct part deflection. Optimize designs for lightweighting. Reduce part cycle time. Compare Learn more. Resources for injection molding simulation.
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