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Callahan, Patrick G.

Publications and source records attributed to Callahan, Patrick G..

Applications of Autonomous Data Collection and Active Learning

Advances in sensors and robotics have dramatically improved the diversity of experimental approaches available to the materials community. Autonomous data collection platforms, either custom-made or commercially available, provide researchers with novel tools with which to probe materials behavior and perform advanced materials characterization. The application of novel control algorithms and active learning approaches can create much more robust experimental data, or can be used to improve the performance of existing characterization tools. Five papers within this special topic focus on experimental and computational methodologies for use in automatic data collection routines for materials characterization. From novel platforms for materials discovery to new statistical frameworks for assessing the autonomous experimentation process, these five papers highlight the diverse range of applications of automation for advancing materials science.

36 MATERIALS SCIENCE↗

Microstructure-Based Estimation of Strength and Ductility Distributions for \(\alpha +\beta \) Titanium Alloys

Titanium alloys are processed to develop a wide range of microstructure configurations and therefore material properties. While these properties are typically measured experimentally, a framework for property prediction could greatly enhance alloy design and manufacturing. In this work, a microstructure-sensitive framework is presented for the prediction of strength and ductility as well as estimates of the bounds in variability for these properties. The framework explicitly considers distributions of microstructure via new approaches for instantiation of structure in synthetic samples. The parametric evaluation strategy, including the finite element simulation package FEpX, is used to create and test virtual polycrystalline samples to evaluate the variability bounds of mechanical properties in Ti-6Al-4V. Critical parameters for the property evaluation framework are provided by measurements of single crystal properties and advanced characterization of microstructure and slip system strengths in 2D and 3D. Property distributions for yield strength and ductility are presented, along with the validation and verification steps undertaken. Comparisons between strain localization and slip activity in virtual samples and in experimental grain-scale strain measurements are also discussed.

36 MATERIALS SCIENCE↗

Slip delocalization and diffusion mediated carbide formation during fatigue of a nickel-base superalloy

Fatigue crack initiation at high temperatures occurs at microscopic fatigue shear bands that form near twin boundaries in polycrystalline nickel-base superalloys that contain minimal metallurgical defects. The associated dislocation sub-structure is complex and dependent on thermal and mechanical conditions. Here we show the formation of fine-scale carbide precipitates along these fatigue shear bands in a nickel-base superalloy during fatigue above 650 °C. The formation of carbide precipitates is studied using calculations of bulk and pipe diffusion. The contribution of pipe diffusion is observed to be critical in the formation of fine-scale carbides and occurs coincident with the delocalization of slip during fatigue.

36 MATERIALS SCIENCE↗