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Intelligent Process Modeling and Optimization of Porosity Formation in High-Pressure Die Casting

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Abstract

In this paper, are presented design and implementation issues of predictive models developed for improving the quality of aluminum die castings by minimizing scrap due to porosity. A predictive model for porosity of casting parts is created using fuzzy systems optimized by genetic algorithm and simulated annealing. High-pressure die casting is a complex process that is affected by a large number of process parameters with influence on casting defects such as porosity. In this study, porosity of casting parts is expressed as a function of counter-pressure, first phase velocity, first phase length, second phase velocity, first cooling period, and second cooling period. It was found that the developed GA- and SA-based fuzzy systems have great predictive capability of porosity in die castings. The second objective of this work was to obtain a group of optimal process parameters leading to minimum porosity in high-pressure die casting using genetic algorithm and simulated annealing as optimal solution finders. The optimal parameters were validated experimentally, and the castings with minimum percentage of porosity were achieved.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, University of Banja Luka, Bulevar vojvode Stepe Stepanovica 71, 78000, Banja Luka, Bosnia and Herzegovina

    Djordje Cica

  2. Faculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, 1000, Ljubljana, Slovenia

    Davorin Kramar

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  1. Djordje Cica
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  2. Davorin Kramar
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Correspondence to Djordje Cica.

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Cica, D., Kramar, D. Intelligent Process Modeling and Optimization of Porosity Formation in High-Pressure Die Casting. Inter Metalcast 12, 814–824 (2018). https://doi.org/10.1007/s40962-018-0213-8

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  • DOI: https://doi.org/10.1007/s40962-018-0213-8

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