The Future—Optimization
Casting process simulation always displays the status quo of its expert user. The user decides if the rigging system or process parameter set led to an acceptable result. Addition- ally, proposals for optimized solutions have to come from the operator.
One of the biggest benefits of the casting process is also its biggest downfall: Everything happens at the same time and is coupled. Changes in one process parameter impact many casting quality defining features during the process, i.e., a change of the pouring temperature does not only change the solidification behavior, it also changes the fluidity of the melt, which can lead to a misrun. The metallurgy of the melt might be impacted, which could lead to changes in the tem- perature balance of the mold or die, which again can lead to problems with overheating or erosion. Multi-objective au- tonomous optimization offers a way out (Fig. 28). It uses the simulation tool as a virtual experimentation field and chang- es pouring conditions, gating designs or process parameters and tries this way to find the optimal route to fulfill the de- sired objective (Fig. 29). Several parameters can be changed and evaluated independently from each other (Fig. 30). Autonomous optimization tools take the classic approach of foundry engineers, to find the best compromise, and use validated physics. This does not only further reduce the need
for trial runs to find the optimal process window, but allows for the detailed evaluation of many process parameters and their individual impact on providing a robust process.
Obviously, only that which can be simulated can be opti- mized. Optimization, therefore, is not a replacement for pro- cess knowledge and expertise. Despite beliefs to the con- trary, the simulation user of the future needs to know the objectives and goals, and especially the quality criteria that are needed to reach these goals. The questions to ask a pro- gram are easy: What is a good gating system? To answer this question, quantitative solutions are required.
Summary
Considering the long tradition of casting, the history of cast- ing process simulation is a small cultural revolution within the industry. The 5,000 year-old art of casting through trial and error is transformed into a transparent, reproducible pro- cess where process parameters can not only be predicted, but also manipulated. Still, not all questions can be answered through simulation, but the current acceptance of this tool in foundries and by casting users confirms that casting process simulation is one of the key innovations of the last thirty years in the foundry world. Simula tion unlocks an enormous potential for cost reduction and leads thereby to an increased competitiveness of foundries.
Figure 28. Optimization Principle. Based on the initial designs optimization variables are defined. Typical variables are geometric changes, i.e. for riser size and/or position, as well as gating systems. At the same time a multi-objective optimization goal is determined, i.e. to achieve the maximum yield in combination with the smallest shrinkage defect. The optimization project runs many simulations to find the best compromise.
International Journal of Metalcasting/Spring 10
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