The forward transformation from face centered cubic austenite to body centered cubic/tetragonal martensite in ferrous alloys can significantly influence the microstructure and mechanical properties of the material. Inferring possible high temperature crystal orientations from observations of ambient temperature transformation microstructures is hindered by parent austenitetwin interactions and scatter in the orientation relationship. This creates a major limitation for studying variant selection phenomena and characterizing microstructural response to high temperature thermomechanical processing conditions. In this work, composition tables are developed that detail the product variant boundary misorientation relationships for parent-parent, parent-twin and twin-twin boundary intersections for the Kurdjumov-Sachs (KS), Nishiyama-Wassermann (NW), and an experimentally determined irrational orientation relationship. The frequently referenced KS and NW orientation relationships produce significantly different results from experimental observations. Furthermore, the introduction of a twin into the parent austenite introduces a substantially larger number of misorientation relationships when the orientation relationship is irrational. The effects of crystal symmetry on misorientation results are determined by considering both body centered cubic and body centered tetragonal martensite structures. Lastly, it is observed that some shared variants are found between twins and parents when assuming cubic symmetry but not tetragonal symmetry. The results and relationships may be useful towards accurate and consistent reconstructions of the parent austenite microstructure from observations of martensite.

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Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system.

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