Optimizing the process parameters of the melt-casting solidification process for energetic materials (MCSPEM) is crucial for improving the quality and efficiency of melt-casting forming systems. The influence of melt-casting process parameters on shrinkage volume (SV) and solidification time (ST) exhibited a highly nonlinear correlation, with significant interactive effects among variables. However, existing process parameters control primarily relies on manual experience, lacking quantitative characterization and co-optimization of MCSPEM parameters concerning SV and ST, leading to inconsistent quality and low efficiency. Therefore, this paper proposed a multi-objective optimization approach to identify the optimal MCSPEM parameters based on adaptive clustering local Kriging (ACLK) and NSGA II-MOHHO algorithm. Firstly, the nonlinear associations of MCSPEM parameters (i.e., pouring temperature, mold preheating temperature, riser insulation temperature and time, jacket insulation temperature and time) with SV and ST were accurately established using the ACLK model. Secondly, a bi-objective optimization model involving SV and ST was established under the process constraints. Thirdly, a hybrid NSGA II-MOHHO algorithm was developed to tackle the bi-objective optimization model, integrating NSGA II's strengths in solution diversity with MOHHO's advantages in adaptive exploration-exploitation switching. Finally, the EWM-TOPSIS method was applied to obtain the optimal MCSPEM parameters from the Pareto front. Case results show that compared with the empirical scheme, the proposed method reduced SV and ST by 54.02% and 16.68%, respectively. This method can recommend the optimal configuration of MCSPEM process parameters and provide quantitative SV and ST information to guide technicians in accurately optimizing and controlling forming defects and efficiency.
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