Optimal Riser Design Of Steel Casting Based On CAE Analysis

With the rapid development of computer and foundry CAE software technology, the role of casting numerical simulation in actual production becomes more important. So as to win the fierce market competition, enterprises have to improve the quality of their products, reduce the costs and shorten the development cycle of new products. Casting simulation software can simulate mold-filling and solidification processes, predict defect and optimize the design process. Then, the best technology solutions will be obtained to meet the needs of casting enterprises for actual productions. So, this paper develops a set of riser optimization system based on CAE analysis.With the guidance of designers, the computations of mold-filling, solidification and riser optimization can be done automatically, and the design of optimal riser can be obtained interactively.Firstly, foundry CAE technology is used to obtain the information of isolated liquid region, predict the location, size and shape of shrinkage and divide the regions for shrinkage feeding.Secondly, this paper further studies the process of casting steel riser and experience of process design, analyzes various design methods for risers of different types, and establishes a mathematical model for common risers.Thirdly, this paper studies the optimization mechanism in genetic algorithms. According to the characteristics of genetic algorithms, a riser optimization system combining the casting process and genetic algorithms has been developed by language C++.Finally, the reliability and accuracy of riser optimization system has been verified by two different theoretic experiments. The optimization for the process of the steam chamber exemplifies the practical applications of optimization system for casting risers.Practical application shows that the system can optimize simple casting processing. It can be proved that the result of optimization is good and stable from the comparison between optimization results and actual results. The system lays the foundation for further exploration and development of a more comprehensive and complex intelligent optimization system of casting processing.