Continually reactivating iterative-projection method for instantiating microstructure from two-point statistics

Processing-structure (PS) linkages play a significant role in materials development. Nevertheless, currently there is a fascinating but of bottleneck task in terms of instantiating microstructure to understand and validate PS linkages. Its challenge lies on the lack of a robust method in computation efficiency and accuracy. Inspired from several advanced techniques such as hybrid input-output (HIO) algorithm, image processing operations and different-phase neighbors-based pixel swapping rule, we proposed a continually reactivating iterative-projection process (CRIP) method to address the challenge above. The output at each iteration in CRIP is continuously improved by eliminating and moving isolated or noise pixels towards an error-reduction direction to activate the iterative-projection process of the next iteration. The performance of the method was examined on two microstructure examples. It shows at least 99.86% improvement in convergence time compared with the classical simulated annealing algorithm and around 6.14 ×10⁻⁴ reduction in error compared with the traditional HIO algorithm. More importantly, CRIP is suitable for the task of instantiating microstructure from periodic two-point statistics predicted by PS linkage, which has been demonstrated by applying it to two experimental datasets of Ni-based superalloys and dual-phase steels. Our proposed method has the advantages of high efficiency, easy operation, reliable accuracy and remarkable generalization ability.