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ABSTRACT Engineering groups of AALLIED Die Casting Company of Illinois, USA, and MAGMA Foundry Technologies, Inc, USA, used state of the art autonomous optimization process simulation software, MAGMASOFT®, to design a die casting process for a large die cast aluminum table base. The die layout and the corresponding process parameter tolerance band were developed with little to no engineering effort. CASTING FUNCTION AND CHALLENGES AALLIED Die Casting Company of Illinois and their global partner were faced with a challenge: to design and build a die cast tool to produce a large die cast aluminum table base. The design of the part was complex in that large bell shapes formed each end of the table, transitioning smoothly to a very slender tubular shape at the casting's center. Structural integrity throughout the casting was of utmost importance in order to meet the functionality of the end product, supporting heavy tabletops, some of which were made of marble. A high gloss coating was applied by heating the casting to a temperature in excess of 350 degrees Fahrenheit. Upon reaching the desired temperature, the casting was quickly submersed and held in a vat of special powder paint until the paint that was in immediate contact with the heated casting liquefied and adhered to it. The casting was then removed from the powder paint vat and cooled in water, solidifying the painted surface. Any porosity close to the surface could propagate blisters during the heating cycle and/or while the casting remained at an elevated temperature in the vat of paint. Due to the nature of the powder paint, it was not economical to strip the castings once blistering has occurred. Therefore prevention of porosity in the casting was of the utmost importance. OBJECTIVES The initial objective was to reduce the air pressure in the casting to prevent porosity. Using this criteria alone, the outcome could be met be increasing the fill time until the air had time to escape. However, this increased time would keep the melt longer in contact to the die surface and cold runs could appear; as the cold material does not weld together, the structural integrity of the casting would not be achieved. To prevent this, the minimum melt temperature had to remain above the solidus temperature at the end of fill, indicating that two parameters had to be considered as part of the casting process. PROCESS DESIGN LIMITATIONS In designing this casting process, two main objectives have to be defined: A: Layout of the mold as position of the casting, runner system, overflows / vents and cooling system B: Process parameter used to fill the cavity and cool the casting The inherently complex and interactive nature of die casting has demonstrated that changing just one of the objectives will influence the complete casting process design. For example, by adding or deleting an overflow, the volume of metal will change, the amount of heat introduced into the die will change, the plunger position to accelerate from the slow into the fast shot will vary as will the filling pattern in the cavity. In the most cases, the engineer defines the process parameter first. He knows the casting volume and estimates a filling time based on the casting dimensions, necessary melt temperature at the beginning and end of the cavity filling and mold temperature. Similarly, he knows the size of the available die casting machine, plunger diameter and pressures for the shot curve and pouring volumes. As these process parameters will be a constant, the designer can use a computer simulation to help in designing the remaining components,, including balancing the runner system, placing overflows and temperature control systems. Once the die is built, the process becomes "written in stone". The success of the project falls upon the engineer's shoulders to ensure that the optimum casting is created from the beginning, so that the die will produce good castings direct from start of production. The reality, however, is that to create a good die the first time out requires a high involvement of the process engineer in the process development. Multiple designs have to be simulated, results evaluated, influencing process parameters determined, and the changes simulated again. While all of the steps require time, identifying and understanding the effects of the influencing parameters - and the resultant effect of each change to those parameters - can be extremely challenging and time-consuming, even with simulation software. With MAGMASOFT®, new simulation capabilities limitations can be significantly reduced. Using one set up, hundreds of designs can be evaluated, weighted, changed and optimized without human interferences. The result is an optimized mold layout and a corresponding process parameter tolerance band. THE OPTIMIZATION PROCESS Based on a given casting design, position and runner system changes can be made to the overflow positions, runner extensions and process parameter. The design combinations possible in MAGMASOFT ® are two for the side overflows (smaller or wider), four for runner extensions (left, right, both, non) and four different positions for the overflows. Process parameter combinations are three slow shot velocities, five transition positions into the fast shot, two accelerations and five different fast shot speeds. All together, there are 4800 possible combinations than can be considered as potential optimized designs. Historically, the simulation time was 75 minutes, per design, on a standard workstation. If all of the possible designs had been simulated, the process would have taken 250 days. In today's fast pace environment, this amount of time is not available. The automatic optimization software is developed to find an optimum by selection and improvement. To begin the optimization, 20 sequences were randomized and selected to build the first generation. Based on the results the next generation of 20 sequences was built. The target was a total of 25 generations or 500 simulations. SIMULATION RESULTS Results can be listed based on the simulated design sequences or as a scatter chart based on the selected objectives. In our example, the selected objectives were maximum air pressure and minimum melt temperature. An examination of the optimization results for the table base design indicated that the 5 best designs for the lowest air pressure still had a melt temperature above the liquidus. For these five designs, neither the size of the selected overflows nor the added ingates were important, but the overflow in the casting middle section had to be in a precise location. The results also indicated that neither the plunger velocity during slow shot nor the acceleration into fast shot were as important as the earlier start of the fast shot. The tendency is a location at the middle of the runner. The fast shot speed itself is the same for all selected designs without any variation and it is clearly an important process parameter to keep in a tide tolerance. Comparison of fast shot velocity to entrapped air confirmed that the faster the velocity, the less time is available to evacuate the cavity, and more air will be entrapped. Options available to the engineer include increasing vent area or the use of vacuum. The results also indicate that to extend the runner on one side or both does not influence the air entrapment or melt temperature significant. The recommendation in this case would be, do not extend the runner system and save some re-melt material. The die layout and process parameter selection at this point would be to keep the overflow layout and add some more venting, do not extend the runner system and keep control on the transition point and plunger velocity of the fast shot as important process parameters. The simulation set up for all these designs is unquestionable longer than for a single simulation, but not significantly so. What will take longer, however, is the length of time needed to run a simulation that is considering 500 designs. It mist be remembered, however, that the evaluation of the 500 designs will be done without the intervention of the engineer, as opposed to the time needed for the engineer to evaluate the results of a single, traditional simulation, make changes, rerun the simulation and compare the results with the ones from before. Similarly, production process tolerances can be implemented and checked frequently and only the results showing areas of greatest interest have to be evaluated by the process team. In our case, a minimum of effort had allowed for multiple simulations to be performed, and yielded results where critical values could be found very easily and adjusted so that the die cast would produce the desired table base. Additional fine-tuning of the design can be performed by shifting the parameter to smaller tolerances and create another design of experiences to further optimize the design. Based on the process knowledge derived from the first optimization program, and thereby already having determined which parameters to change or keep constant, the rerun would need just a couple of dozen iterations. The result is a design that can reach the production floor faster and more efficiently, reducing costs and increasing customer satisfaction. REFERENCES Jay Krueger, AALLIED Die Casting Company of Illinois, Franklin Park, IL 60131, USA & R. Kind, Magma Foundry Technologies, Inc, Schaumburg, IL, "Autonomous Design and Process Optimization of a Die Cast Tool to Produce an Aluminum Table Base", NADCA, CastExpo '08, 2008 Atlanta, Georgia R. Kind, Ke Roth, Magma Foundry Technologies, Inc, Schaumburg, IL, Phone: 847-969-1001, Web: www.magmasoft.com
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