Novel methodology for casting process optimization using Gaussian process regression and genetic algorithm

High pressure die casting (HPDC) is a versatile material processing method for mass-production of metal parts with complex geometries, and this method has been widely used in manufacturing various products of excellent dimensional accuracy and productivity. In order to ensure the quality of the components, a number of variables need to be properly set. A novel methodology for high pressure die casting process optimization was developed, validated and applied to selection of optimal parameters, which incorporate design of experiment (DOE), Gaussian process (GP) regression technique and genetic algorithms (GA). This new approach was applied to process optimization for cast magnesium alloy notebook shell. After being trained, using data generated by PROCAST (FEM-based simulation software), the GP model approximated well with the simulation by extracting useful information from the simulation results. With the help of MATLAB, the GP/GA based approach has achieved the optimum solution of die casting process condition settings.

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