Compositionally Grading Alloy Stacking Fault Energy using Autonomous Path Planning and Additive Manufacturing with Elemental Powders
James Hanagan, Nicole Person, Daniel Salas, Marshall Allen, Wenle Xu,, Daniel Lewis, Brady Butler, James D. Paramore, George Pharr, Ibrahim Karaman,, Raymundo Arroyave

TL;DR
This paper demonstrates a novel approach combining autonomous path planning and additive manufacturing to create compositionally graded alloys with tailored stacking fault energy, enabling rapid alloy design and microstructure control.
Contribution
It introduces a new framework applying robotics-inspired path planning to alloy design, integrating it with additive manufacturing for microstructure and property optimization.
Findings
Successfully created a compositionally graded alloy with a controlled SFE gradient.
Observed martensitic transformation despite equilibrium phase predictions.
Highlighted limitations of CALPHAD in predicting phase stability during rapid processing.
Abstract
Compositionally graded alloys (CGAs) are often proposed for use in structural components where the combination of two or more alloys within a single part can yield substantial enhancement in performance and functionality. For these applications, numerous design methodologies have been developed, one of the most sophisticated being the application of path planning algorithms originally designed for robotics to solve CGA design problems. In addition to the traditional application to structural components, this work proposes and demonstrates the application of this CGA design framework to rapid alloy design, synthesis, and characterization. A composition gradient in the CoCrFeNi alloy space was planned between the maximum and minimum stacking fault energy (SFE) as predicted by a previously developed model in a face-centered cubic (FCC) high entropy alloy (HEA) space. The path was designed…
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Taxonomy
TopicsMetallurgy and Material Forming · Advanced Surface Polishing Techniques · Mineral Processing and Grinding
