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Multi-objective optimization of a cast-preform shape for a magnesium alloy forging application

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Careful consideration of the preform shape is essential when designing a forging process. A high-quality forging process must promote process-related grain refinement and an unbroken grain flow devoid of cavities or folding, in addition to minimizing the amount of generated flash while achieving a complete die fill. The desired forged part properties can be obtained by optimizing the preform shape. However, three-dimensional shape optimization presents challenges in design generation and design evaluation due to the resource-intensive demands of each task. To address these challenges, we propose a multi-objective optimization framework consisting of a parametric computer-aided design (CAD) model for shape generation, data-driven models for shape evaluation, and a multi-objective evolutionary optimization algorithm to search the design space effectively. This computational framework is used to evolve an optimal preform shape, which was ultimately cast using permanent mold casting (PMC) and then hot forged under elevated temperature conditions. We compared the forging outcome of the optimal preform with a baseline cylindrical billet which was produced according to the same sequence of manufacturing steps. Comparative analysis of the laboratory-scale forging results revealed that the cast-preform and cast-billet produced about 6% and 12% flash material, respectively. Quasi-static tensile and stress-controlled cyclic tests were also conducted to evaluate mechanical properties. While comparable yield and ultimate tensile strengths were observed in both forgings, a significant increase in fracture strain was observed in the preform forging, suggesting improved toughness. In general, the forging outcome of the optimized preform proved to be superior to the billet forging.

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The authors would like to acknowledge Lucian Blaga, Bruce Williams, and Jonathan McKinley for conducting the laboratory-scale forging experiments.


Financial support is provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), under the Strategic Partnership Grants for Projects (SPG-P) with contributions from Multimatic Technical Centre and CanmetMATERIALS.

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Correspondence to Tharindu Kodippili.

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Figure 9 shows the prediction accuracy of the MLP networks on the test data.

Fig. 9
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MLP predictions on test data: average effective plastic strain (AEPS), flash, and fill percentages

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Kodippili, T., Azqadan, E., Jahed, H. et al. Multi-objective optimization of a cast-preform shape for a magnesium alloy forging application. Int J Adv Manuf Technol 129, 3221–3232 (2023).

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