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A CASTING YIELD OPTIMIZATION CASE STUDY: FORGING RAM P. Kotas, C. Tutum and J. Hattel


Technical University of Denmark, Lyngby, Denmark O. Šnajdrová


Vitkovice Heavy Machinery A.S., Ostrava, Czech Republic J. Thorborg


Technical University of Denmark, Lyngby, Denmark MAGMA GmbH, Aachen, Germany


Copyright © 2010 American Foundry Society Abstract


This work summarizes the findings of multi-objective optimization of a gravity sand-cast steel part for which an increase of casting yield via riser optimization was considered. This was accomplished by coupling a casting simulation software package with an optimization module. The benefits of this approach, recently adopted in the foundry industry worldwide and based on fully automated computer optimization, were demonstrated. First, analyses of filling and solidification of the original casting design were conducted in the standard simulation environment to determine potential flaws and inadequacies. Based on the initial assessment, the


Introduction


Today, there is a general trend to reduce production costs as much as possible in order to stay competitive and attrac- tive for potential and current customers. Production is no longer governed only by experience-based innovation or trial-and-error method. In this context, virtual prototyping and testing by numerical simulation offers an efficient way of reducing product development time, reducing costs of producing prototypes, shortening lead time and, above all, eliminating scrap.


Metalcasting process simulation is used to provide detailed information about mold filling, solidification and solid state cooling, as well as, information about the local microstruc- ture, non-uniform distribution of mechanical properties and subsequently residual stress and distortion build-up.1-9 Casting simulation tries to use physically realistic models without overtaxing the computer. At the same time the simulations need to give applicable results in the shortest time possible. Unfortunately, numerical simulations can only test one “state”, while conclusions from calculations or subsequent optimization still require an engineer’s in-


International Journal of Metalcasting/Fall 10


gating system was redesigned and the chills rearranged to improve the solidification pattern. After these two cases were evaluated, the adequate optimization targets and constraints were defined. One multi-objective optimization case with conflicting objectives was considered in which minimization of the riser volume together with minimization of shrinkage porosity and limitation of centerline porosity were performed.


Keywords: casting process simulation, genetic algorithms, riser volume optimization, casting yield, steel casting, centerline porosity, macro-porosity


terpretation and decision after each of the simulation runs. Understanding the process enables a foundry engineer to make decisions that can affect both the part and the rigging to improve the final quality.


The objectives which drive designers are generally well de- fined: improve the component quality, achieve homogeneous mechanical characteristics, maximize the casting yield, in- crease the production rates, etc. It may sound easy, but the truth is that in reality it is very complex and time consuming to achieve all these objectives at the same time, due to the high number of variables involved. In many foundries, the only applied optimization is still based on experience and thus on the trial-and-error method. When using numerical simulation, only a virtual casting is spoiled, in the case of an error. No raw material is wasted, no mould is produced and, above all, no production loss is experienced.


Recently, rapid development of high performance comput- ing has substantially shortened the calculation time needed for one variant of the casting process to be analyzed. It is feasible to calculate numerous versions and layouts in almost unlimited configurations over night. The advantage


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