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Surrogate-based microstructural optimization was applied to describe the relationship between local microstructural patterns and particle damage in wrought 2024 aluminium alloy. A support vector machi...ne was used to realise high-accuracy optimisation from a limited number of high-computational-cost image-based simulation results. The methodology integrated thoroughgoing microstructural quantification, a couple of coarsening processes, and surrogate modelling. The following three objective functions were defined: the maximum principal stress in particles, the equivalent plastic strain, and the stress triaxiality in the matrix. A number of design parameters were comprehensively prepared that quantitatively expressed the size, shape, and spatial distribution of particles and pre-existing micro pores in numerous ways. The number of design parameters was then reduced from 86 to 4 for each objective function during the coarsening process. The surrogate model provided the dependency of particle damage for the size, shape, and spatial distribution of particles and micro pores in the form of a multi-dimensional response surface. It has been established that micro void formation can be described using the simple volume and surface area of particles through the elevation of particle stress and the increase in equivalent plastic strain in the matrix, and the spatial distribution of pre-existing micro pores is of crucial importance for micro void growth through the elevation of stress triaxiality in the matrix. The proposed material microstructure optimisation method provides relationships between complex local microstructural patterns and material properties that are not available from existing material development approaches. It is a computationally inexpensive and reasonable method that can optimise the complex and irregular microstructures of real materials with high efficiency and accuracy.続きを見る
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