arXiv subjects

Allison M. Beese

Publications and source records attributed to Allison M. Beese.

21 records · Page 2Linked to original sources

arXiv2021

Generative deep learning as a tool for inverse design of high-entropy refractory alloys

Generative deep learning is powering a wave of new innovations in materials design. In this article, we discuss the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory high-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships in order to generate novelty on demand, making them a valuable tool for materials informatics.

cond-mat.mtrl-sci↗

arXiv2021

Tensile behavior of stainless steel 304L to Ni-20Cr functionally graded material: experimental characterization and computational simulations

This study presents experimental and computational analyses of the phase composition and mechanical behavior of a functionally graded material (FGM) grading from 100 vol% 304L stainless steel (SS304L) to 100 vol% Ni-20Cr (NiCr) in 10 vol% increments. The mechanical behavior of the FGM was evaluated by extracting tensile specimens from SS304L-rich and NiCr-rich regions of the FGM, with the former behaving in a ductile manner and the latter being relatively brittle. The brittle response of the NiCr-rich sample was explained by the presence of the eutectic FCC+BCC phase in this sample, as identified by Scheil-Gulliver simulations along with experimental phase and composition analyses. The spatially varying yield and flow behavior of the FGM was determined through comparison of finite element analysis simulations with experimentally measured stress-strain curves and surface strain contours. The results highlight that the overall ductile behavior of SS304L and brittle behavior of NiCr are preserved in the SS304L/NiCr FGM but the formation of the brittle eutectic phase further reduced the ductility within the regions where it formed (90 vol% NiCr/10 vol% SS304L).

cond-mat.mtrl-sci↗

arXiv2020

Scheil-Gulliver simulations for the design of functionally graded alloys by additive manufacturing using pycalphad

Additive manufacturing (AM), through directed energy deposition, supports planned composition changes between locations within a single component, allowing for functionally graded materials (FGMs) to be developed and fabricated. The formation of deleterious phases along a particular composition path can cause significant cracking during the AM build process that makes the composition path unviable to produce these FGMs, but it is challenging to predict which phases will be present in as-built additively manufactured parts by analyzing only equilibrium phase relations. Solute segregation during solidification can lead to the formation of non-equilibrium phases that are stable at compositions far from the nominal composition of the melt, leading to crack formation. In this work, we developed a Scheil-Gulliver simulation tool based on pycalphad. We used this tool to compare the non-equilibrium phases predicted to form during solidification using the Scheil-Gulliver model with experimentally measured phases at several locations with different composition in a Ti-6Al-4V to Invar-36 FGM and a commercially pure Ti to Invar-36 FGM. We showed that the phases predicted to form by the Scheil-Gulliver model outperform the predictions made by assuming equilibrium solidification. Further, we demonstrated the use of our Scheil-Gulliver simulation tool as a method of screening potential FGM pathways by calculating the solidification phase fractions along the experimental gradient path in composition space.

cond-mat.mtrl-sci↗