Phase Transformations and Microstructural Evolution: Ferrous Alloys
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Phase Transformations Committee
Program Organizers: Mohsen Asle Zaeem, Colorado School of Mines; Ramasis Goswami, Naval Research Laboratory; Saurabh Puri, Microstructure Engineering; Eric Payton, University of Cincinnati; Megumi Kawasaki, Oregon State University; Eric Lass, University of Tennessee-Knoxville
Tuesday 8:00 AM
March 1, 2022
Location: Anaheim Convention Center
Session Chair: Megumi Kawasaki, Oregon State University
Kinetics of Phase Transformations in Boron-containing 304L Stainless Steel: Erin Barrick1; James (Tony) Ohlhausen1; Donald Susan1; Khalid Hattar1; Jack Herrmann1; Peter Duran1; Jeffrey Rodelas1; Charles Robino1; 1Sandia National Laboratories
Boron-rich phases in austenitic stainless steels can promote liquation cracking in the heat-affected zone during welding. Previous work at Sandia has demonstrated that at boron concentrations below 20 wt. ppm, 304L stainless steel that undergoes certain heat treatments is susceptible to cracking. The kinetics of phase transformations (e.g. ferrite dissolution, boride redistribution, etc.) during heat treatment that generate the crack susceptible microstructure are currently unknown. Heat treatments were performed in a Gleeble thermophysical simulator across a range of temperatures and cooling rates to investigate these transformations. Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) was used in combination with scanning electron microscopy to observe the distribution of chromium borides. Automated image analysis techniques were used to quantify microstructural changes during heat treatments. These results will be used to enable quantitative prediction of thermal processing conditions to avoid weld cracking. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525
Structural Evolution by Grain Refinement and Relaxation upon Heating of an Additive-manufactured 316L Stainless Steel: Jae-Kyung Han1; Xiaojing Liu2; Yusuke Onuki3; Yulia Kuzminova4; Stanislav Evlashin4; Klaus-Dieter Liss2; Megumi Kawasaki1; 1Oregon State University; 2Guangdong Technion - Israel Institute of Technology; 3Ibaraki University; 4Skolkovo Institute of Science and Technology
In this report, structural evolution through nanostrcuturing is examined by a series of X-ray diffraction analysis and the structural relaxation behavior is examined by in-situ neutron diffraction under heating on the nanostructured additive-manufactured 316L stainless steel. Significant structural changes occur in a very early stage of nanostructuring and it is attributed to severe lattice distortion by the excess of dislocations and defects introduced by the post-printing processing. The sequential information on the structure evolution during in-situ neutron diffraction analysis upon heating provides the texture development, linear thermal lattice expansion, and stress relaxation behaviors of the nanocrystalline steel with increasing temperature up to 1300K, while these structural changes are in contrast to the steel without nanostructuring. Together with the hardness measurements after heating, the results of structural evolution are interpreted to describe microstructural recovery, recrystallization and grain growth behaviors and the thermal stability of the nanocrystalline stainless steel.
Cluster Evolution and Phase Transformation in Austenitic High-Cr Stainless Steel: A Comparison of Thin Film and Bulk Geometries: Po-Cheng Kung1; Jian-Min Zuo1; Jessica Krogstad1; 1University of Illinois Urbana Champaign
To tailor the mechanical properties of metals to various use cases through precipitation hardening, it is crucial to understand how short-range structures evolve into precipitates in different condition. The present work investigates the effect of sample dimensions on the evolution of Cr short-range clusters in austenitic Fe-21Cr-6Ni-9Mn-0.3N stainless steel between 300-450 °C. A thin-film sample was heated in-situ at 450 °C in TEM, revealing that sphere-like Cr clusters, which were found in steel before heating, developed facets and transformed into the MgCu2 Laves phase. This was confirmed through in-depth diffraction analysis and high-resolution electron microscope images. On the contrary, bulk steel sample heated in a furnace presented an alternative phase transformation product and no faceting. For both samples, the cluster structures and the strain fields they induced were probed by 4D-STEM to understand the phase transformation and potential strengthening mechanisms of this steel.
Phase Separation under Irradiation in FeNi and Low-alloyed Steels: Quentin Tence1; Maylise Nastar1; Estelle Meslin1; Isabelle Mouton2; Brigitte Décamps3; 1CEA Saclay, Service de Recherche en Métallurgie Physique, Université Paris Saclay; 2CEA Saclay, Service de Recherche en Métallurgie Appliquée, Université Paris Saclay; 3CNRS, IJC-Lab, Université Paris Saclay
Lattice point defects induced by irradiation are recognized to have a significant effect on the solute redistribution and stability of phases in alloys. We investigate their kinetic and thermodynamic effects in Fe-Ni model alloys of ferrite and austenite. We conduct ion irradiations at JANNUS facilities on FCC Fe-Ni alloys with Ni concentrations ranging from 30 to 50 at.% under several radiation fluxes, fluences, and temperatures. TEM, STEM-EDS and APT analysis revealed various decomposition states, which all include 50 at.% Ni rich dislocation loops and significant changes of the matrix composition depending on the irradiation conditions. To model the effect of a permanent supersaturation of point defects on the dynamic equilibrium between phases, we rely on a CALPHAD database and introduce a non equilibrium point defect contribution. The resulting metastable compositions are consistent with the experimental observations. We extend this approach to Fe-based dilute model alloys of low-alloyed steels.
Leveraging EBSD Data for Phase Transformation Product Quantification in a Low Carbon Steel by Deep Learning: Simon Breumier1; Tomas Ostormujof2; Nathalie Gey3; Audrey Couturier4; Pierre-Emmanuel Aba-perea1; Bianca Frincu4; Natalia Loukachenko4; Lionel Germain2; 1Institut de Recherche Technologique Matériaux, Métallurgie et Procédés - 4, rue Augustin Fresnel F-57078 Metz France; 2Laboratory of Excellence on Design of Alloy Metals for Low-mAss Structures (DAMAS), Université de Lorraine, France; 3Université de Lorraine, CNRS, Arts et Métiers Paris Tech, LEM3, F-57000 Metz, France; 4INDUSTEEL (ArcelorMittal), Centre de Recherche des Matériaux du Creusot (CRMC), Le Creusot, France
Quantification of the different phases in steels is challenging. It usually relies on micrographs, which features are sensitive to the imaging conditions (etchant, detector used…) and does not always provide enough contrast to differentiate some of the transformation products. In contrast, EBSD analyses provides a wealth of various features specific to the material’s transformation history: Orientation Relationship, low angle misorientation densities, habit planes and diffraction contrast. This work investigates the application of deep learning approaches to quantify different phase transformation products in an industrial steel using EBSD data.A U-NET convolutional neural network was developed for automatic segmentation of bainite, martensite and ferrite in a low carbon steel, using either the orientations, the misorientations and different pattern quality indicators provided by EBSD. Alternatively, semi-supervised approaches such as generative adversarial networks were explored to achieve similar results without any manual labelling before training.