Over thousands of years, foundry technology did not change much until the dawn of the computer age. Electronics allow building fully automatic production processes with handling robots and monitoring systems, increasing quality and productivity by reducing hard working labor forces. With autonomous optimization tools, research and development is taken out of the direct production area. Trial and error is not done on the plant floor anymore with production machines wasting time, casting volumes and costs. In a very short time, optimization has taken over the process of simulation, eliminating manual corrections and the high cost of wasted engineering time. Engineers today define a process with tolerance windows and the objectives the process has to achieve, while optimization will come up with the best results to do so. |
Fill Parameter Optimization The filling process is determined by plunger movements pushing the liquid melt into the die cavity. Melt is poured through a hole into a tube, called a shot sleeve, which is closed on one side by the plunger and on the other by the die. After filling the sleeve to a specific level, the plunger slowly moves forward pushing the melt towards the die. When the melt reaches the casting cavity, the plunger is accelerated to high speed to fill the cavity in 1/10th of a second. The filling speed is critical as filling slower can allow the melt to get too cold while filling faster does not give the trapped air time to escape. Melt that is too cold or contains too much air will reduce the casting quality. Finding the right compromise is an important task. Using variations of these fill parameter is an easy task for the optimization software. The engineer simply creates a template including the parameter to change, the step variation and tolerances, and the software takes over. It selects a starting design and changes the parameter according the variations and tolerances for single simulations. Interpretations of filling results are done automatically based on the melt or die temperature, fill time or air volume values at end of fill. A once tedious and time-consuming, but critical, engineering task has now been relegated to an automatic and more accurate process.
Simulating all possible variations and comparing them would be nice, but based on the high number of possible variations it is, in most cases, impractical. For example, a simulation having only six parameters and five variations would end in 7,776 simulation runs. Assuming an average time of three minutes per simulation, the entire project would take over sixteen days to complete, which is time that most metal casting engineers do not have anymore. MAGMAfrontier® uses genetic optimization algorithm. As in the biological world, the evolutionary process of autonomous optimization occurs over several calculation generations. Based on the defined objectives, such as high temperature and/or low air volumes, a generic algorithm creates new variations of the filling parameter. The process is repeated until design modifications do not lead to additional improvements.
Component Optimization Getting a new project or the opportunities to change the casting or die design, such as adding fins, more wall stock, overflow locations, gate locations, ingate variations, cooling or heating lines, die materials, etc., calls for action in the engineering group. Selecting the best design out of all the given possibilities is often connected with failure, even when those involved are experienced. Autonomous optimization, on the other hand, opens the possibility to simulate and find the right elements for the casting projects. Simply by designing these single objects and switching them from simulation to simulation is an easy task for the software. For example, a particular set of components is calculated and the runner model is selected to be replaced by one with more or less branches; without engineering intervention, the software automatically loads the new design and calculates it. Based on the defined objective, the computer will find the best component configuration in a short time. What’s left for the engineers to do is to evaluate the best simulation results, then release the optimal design to build the tool.
Optimization with Parametric Objects The most exciting alternative is to manipulate objects by simply changing numerical values. Instead of drafting and designing multiple runner systems to be selected for the optimization, runner systems can be manipulated using numerical parameters. Ingate areas can be increased or reduced simply by adjusting the parameter for the ingate thickness or width. Balancing a runner system for one or multiple cavity dies is easily done by changing the numerical values for runner length, angle and/or direction. With the given templates provided in MAGMAfrontier®, a runner system can be defined, built, and prepared for optimization in minutes, including variations of multiple runner branches and ingates. Overflow and vent dimensions can be adjusted in size and volume to reduce air entrapment and porosity. Balancing the thermal profile of a die to reduce die flashing, cold runs and to increase die life, placing cooling lines is crucial, but becomes a simple task when optimization tools are used. Line start and end locations are defined numerical and can be easily changed and dependencies can be readily defined. By changing the line temperature values, cooling and heating can be simulated as necessary.
Ralf Kind / Ke Roth MAGMA Foundry Technologies, Inc, Schaumburg, IL, USA
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