Local Maxima in Martensite Start Temperatures in the Transition Region between Lath and Plate Martensite in Fe-Ni Alloys.

In the binary Fe-rich Fe-Ni system, martensite start temperatures MS decrease from 500 to 200 K when Ni concentrations increase from 20 to 30 at.%. It is well known that alloys with Ni concentrations below 28.5 at.% exhibit lath martensite (LM) microstructures (athermal transformation, small crystals, accommodation by dislocations). Above this concentration, plate martensite (PM) forms (burst-like transformation, large crystals, accommodation by twins). The present work is based on a combination of (i) ingot metallurgy for the manufacturing of Fe-Ni alloys with varying Ni-concentrations, (ii) thermal analysis to measure phase transformation temperatures with a special focus on MS, and (iii) analytical orientation imaging scanning electron microscopy for a quantitative description of microstructures and crystallographic features. For Ni-concentrations close to 28.5 at.%, the descending MS-curve shows a local maximum, which has been overlooked in prior works. Beyond the local maximum, MS temperatures decrease again and follow the overall trend. The local maximum is associated with the formation of transition martensite (TM) microstructure, which exhibits LM and PM features. TM forms at higher MS temperatures, as it is accommodated by simultaneous twinning and dislocation slip. An adopted version of the Clausius-Clapeyron equation explains the correlation between simultaneous accommodation and increased transformation temperatures.

Application of the Maximum Flow-Minimum Cut Algorithm to Segmentation and Clustering of Materials Datasets.

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.