Florez L.,Sigma Engineering GmbH |
Schwittay V.,SIGMA Engineering GmbH |
Proske M.,SIGMA Plastic Services Inc.
Kunststoffe International | Year: 2014
Why get caught up in a never-ending cycle of corrective measures when simulating injection molding long before production commences represents a more reliable approach to mold and process design? A virtual injection molding process which has been on the market for the last year and has now passed the first proving trials with Converters provides that footing. © Carl Hanser Verlag, Munich. Source
Sigma Engineering has developed a new way of minimizing particle segregation in metal injection molding (MIM). The company plans to present the technology at the World PM2016 Congress & Exhibition, taking place in Hamburg, Germany, from 9-13 October. According to the company, simulation is a well-established tool in MIM, as it accurately predicts flow and thermal-related effects in the green part molding. Recently, Sigmasoft Virtual Molding has developed a number of new features to predict particle segregation, one of the most common problems associated to MIM. Particle segregation causes surface defects, and once the green part is sintered, the differences in density lead to inhomogeneous shrinkage and therefore to warpage. Particle segregation is mainly a process-driven effect, caused by shear. Therefore, process parameters and part gating are critical. Sigma suggests that autonomous optimization can be used to reduce the appearance of particle segregation. Figure 1 shows particle concentration for a very simple part. Three scenarios are considered: on the left, the part is filled with a short filling time. The part in the center was simulated with a long filling time, and the right part shows the particle concentration for the short time but with a different gate position. For each variation the segregation pattern is quite different. ‘To understand the problem it makes sense to run various simulations in a design of experiments (DOE),’ said Timo Gebauer, Sigma’s chief technical officer. ‘This can help figuring out the correlations of the different boundary conditions and the influence on the observed result.’ In the optimization, an even particle distribution is the main goal, and both filling time and gate geometry are varied in a first study. As seen in Figure 2, the difference in particle concentration diminishes with increasing filling time, and there is a minimum achieved with a given offset to the middle plane of the part for the gate position. To make the simulation feasible for an industrial part and mold, an approach called ‘autonomous optimization’ is used. Using a similar strategy to the one previously described, the problem is solved in parts through several optimization generations. This reduces the amount of calculations necessary to find an optimum solution. According to the company, the approach can be used to minimize particle segregation in real products. This story uses material from Sigma, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier.