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DOE OSTI2024

Computer vision models and advanced TEM imaging for microstructures of irradiated AM316 stainless steels

Advancements were made in automating microscopy-based material characterization, particularly in studying irradiation effects on additively manufactured (AM) materials using machine learning (ML) and computer vision (CV). These automation efforts address the challenges of analyzing complex microstructures, accelerating the detection of irradiation-induced defects. Two CV models were developed at Argonne National Laboratory (ANL) to enhance transmission electron microscopy (TEM) analysis of irradiated AM 316 stainless steel. The first model focused on the detection of irradiation-induced dislocation loops, which contribute to material hardening and embrittlement. These loops, categorized as faulted or perfect, were automatically detected and classified using a Mask R-CNN model trained on TEM images from both in-situ and ex-situ ion irradiation experiments. The model achieved high accuracy, with precision, recall, and F1 scores of 0.839, 0.734, and 0.776, respectively, demonstrating its effectiveness in analyzing dislocation loops in irradiated AM materials. The second CV model was developed to analyze the size and wall thickness of dislocation cells in laser powder bed fusion (LPBF) 316 stainless steel. Using a U-Net++ architecture with EfficientNet as the encoder, the model was trained on TEM images to segment and measure cell size and wall thickness.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718

Additive manufacturing (AM) is revolutionizing almost all industries through the production of intricate geometries previously prohibited by cost or machinability. Ni-based superalloys form a primary alloy class for high temperature applications in the petrochemical, aerospace, and nuclear industries because of their intrinsic resistance to creep and the ability to heat treat the superalloy for high strength. Despite these attractive properties, the extreme work hardening of Ni-based superalloys makes traditional manufacturing of complex shapes difficult and thus, these alloys are an attractive target for AM. Superalloy 718 was chosen as an example superalloy because of the wide variety of precipitates that can form within its composition space from the repetitive heating and cooling cycles of the AM process. The precipitates and other microstructure features, such as grain boundaries and dislocations, will dictate the mechanical properties and thus, there is an extensive challenge to characterize the size, number density, composition, and volume fraction of each microstructural feature from AM fabrication using analytical electron microscopy. This work focused on the application of a pixel-wise classification machine learning (ML) model called a dynamic segmentation convolutional neural network (DSCNN) to identify the microstructural features of an as-fabricated additively manufactured superalloy 718.

Taller, Stephen↗

DOE OSTI2017

A microstructure sensitive grain boundary sliding and slip based constitutive model for machining of $\mathrm{Ti}$-$\mathrm{6Al}$-$\mathrm{4V}$

We report a composite dual phase internal state variable constitutive model was developed for Ti-6Al-4V. The proposed model includes diffusion assisted grain boundary sliding based physics in addition to a traditional slip-based plasticity. Influence of microstructure on the flow stress is introduced via dislocation density and mean grain size internal state variables. The dislocation density evolves according to a physics based law that considers dislocation nucleation and annihilation processes. Grain refinement is driven by dynamic recrystallization, which is modeled phenomenologically. The model is calibrated with uniaxial stress–strain data that ranges between quasi-static and dynamic rates across a wide range of temperatures. Validation against machining data shows that the model predicts chip segmentation frequency, machining forces, and tool temperatures reasonably well. The newly introduced grain boundary sliding physics was found to dominate deformation following sufficient grain refinement. This deformation mode provides softening at the constitutive level without the need for invoking damage based softening mechanisms. This physical interpretation is something that has not previously been explored in the machining literature.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Melt-Centrifuged (Bi,Sb) 2 Te 3 : Engineering Microstructure toward High Thermoelectric Efficiency

Microstructure engineering is an effective strategy to reduce lattice thermal conductivity (JG) and enhance the thermoelectric figure of merit (zT). Through a new process based on melt-centrifugation to squeeze out excess eutectic liquid, microstructure modulation is realized to manipulate the formation of dislocations and clean grain boundaries, resulting in a porous network with a platelet structure. In this way, phonon transport is strongly disrupted by a combination of porosity, pore surfaces/junctions, grain boundaries, and lattice dislocations. These collectively result in a ≈60% reduction of JG compared to zone melted ingot, while the charge carriers remain relatively mobile across the liquid-fused grains. This porous material displays a zT value of 1.2, which is higher than fully dense conventional zone melted ingots and hot pressed (Bi,Sb) 2 Te 3 alloys. A segmented leg of melt-centrifuged Bi 0.5 Sb 1.5 Te 3 and Bi 0.3 Sb 1.7 Te 3 could produce a high device ZT exceeding 1.0 over the whole temperature range of 323-523 K and an efficiency up to 9%. Here, we demonstrate a method for synthesizing high-efficiency porous thermoelectric materials through an unconventional melt-centrifugation technique.

36 MATERIALS SCIENCE↗

NASA NTRS1984

Modeling of the surface static displacements and fault plane slip for the 1979 Imperial Valley earthquake

Three-dimensional finite element modeling techniques are used to synthesize geodetic and seismological results for 1979 Imperial Valley earthquake. The strategy pursued consists of two principal steps. In the first step, the seismologically-derived coseismic fault slip is taken as a function of position in the fault plane and is applied directly to the three-dimensional dislocation model. In the second step, a physical model of stresses and constitutive parameters is perturbed so as to reproduce the observed fault slip. Hence, the principal features of the coseismic slip pattern are explained by a stress-driven fault model in which: (1) a spatially unresolved asperity is found equivalent to a stress drop of 18 MPa averaged over an area of 15 sq km, and (2) driving stress is essentially absent on the fault segment overlapping the 1940 earthquake rupture zone.

Slade, M. A.↗

DOE OSTI2019

Plastic recovery and self-healing in longitudinally twinned SiGe nanowires

This paper reports on plastic recovery and self-healing behavior in longitudinally-twinned and [112] orientated SiGe nanowire (NW) beams when they are subjected to large bending strains. The NW alloys are comprised of lamellar nanotwin platelet(s) sandwiched between two semi-cylindrical twins. The loading curves, which are acquired from atomic force microscope (AFM) based three-point bending tests, reveal the onset of plastic deformation at a characteristic stress threshold, followed by further straining of the NWs. This ductility is attributed to dislocation activity within the semi-cylindrical crystal portions of the NW, which are hypothesized to undergo a combination of elastic and plastic straining. On the other hand, the lamellar nanoplatelets undergo purely elastic stretching. During the unloading process, the release of internal elastic stresses enables dislocation backflow and escape at the surface. As a result, the dislocations are predominantly annihilated and the NW samples evidenced self-healing via plastic recovery even at ultra-large strains, which are estimated using finite-element models at 16.3% in one of the tested devices. Finite element analysis also establishes the independence of the observed nanomechanical behavior on the relative orientation of the load with respect to the nanoplatelet. This first observation of reversible plasticity in the SiGe material system, which is aided by a concurrent evolution of segmented elastic and plastic deformation within its grains during the loading process, presents an important new pathway for mechanical stabilization of technologically important group-IV semiconductor nanomaterials.

36 MATERIALS SCIENCE↗

NASA NTRS1986

Dislocation model for aseismic crustal deformation at Hollister, California

A model of crustal deformation during the interseismic phase is developed and applied (using the improved Bayesian inversion algorithm described by Jackson and Matsu'ura, 1985) to trilateration data for the USGS Hollister (CA) network. In the model, rigid blocks in motion relative to each other experience friction only in a brittle upper zone, while their ductile lower zones slide freely; the Hollister model comprises five blocks and nine rectangular fault patches. The data and results are presented in tables, graphs, and maps and characterized in detail. The model predicts steady block motion on time scales between 10 yr and 1 Myr, with net motion across the San Andreas/Calaveras fault system 38 + or - 3 mm/yr and brittle/ductile transition depths ranging from 0.4 to 11 km. Two San Andreas segments with higher probabilities of moderate-to-large earthquakes are identified.

Matsuura, Mitsuhiro↗