Publications

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The mechanical behavior of partial-penetration laser welds exhibits significant variability in engineering quantities such as strength and apparent ductility. Understanding the root cause of this variability is important when using such welds in engineering designs. In Part II of this work, we develop finite element simulations with geometry derived from micro-computed tomography (μCT) scans of partial-penetration 304L stainless steel laser welds that were analyzed in Part I. We use these models to study the effects of the welds’ small-scale geometry, including porosity and weld depth variability, on the structural performance metrics of weld ductility and strength under quasi-static tensile loading. We show that this small-scale geometry is the primary cause of the observed variability for these mechanical response quantities. Additionally, we explore the sensitivity of model results to the conversion of the μCT data to discretized model geometry using different segmentation algorithms, and to the effect of small-scale geometry simplifications for pore shape and weld root texture. The modeling approach outlined and results of this work may be applicable to other material systems with small-scale geometric features and defects, such as additively manufactured materials.

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Cryogenic plasma focused ion beam (PFIB) electron microscopy analysis is applied to visualizing ex situ (surface industrial) and in situ (subsurface geologic) carbonation products, to advance understanding of carbonation kinetics. Ex situ carbonation is investigated using NIST fly ash standard #2689 exposed to aqueous sodium bicarbonate solutions for brief periods of time. In situ carbonation pathways are investigated using volcanic flood basalt samples from Schaef et al. (2010) exposed to aqueous CO₂ solutions by them. The fly ash reaction products at room temperature show small amounts of incipient carbonation, with calcite apparently forming via surface nucleation. Reaction products at 75° C show beginning stages of an iron carbonate phase, e.g., siderite or ankerite, common phases in subsurface carbon sequestration environments. This may suggest an alternative to calcite in carbonation low calcium-bearing fly ashes. Flood basalt carbonation reactions show distinct zonation with high calcium and calcium-magnesium bearing zones alternating with high iron-bearing zones. The calcium-magnesium zones are notable with occurrence of localized pore space. Oscillatory zoning in carbonate minerals is distinctly associated with far-from-equilibrium conditions where local chemical environments fluctuate via a coupling of reaction with transport. The high porosity zones may reflect a precursor phase (e.g., aragonite) with higher molar volume that then “ripens” to the high-Mg calcite phase-plus-porosity. These observations reveal that carbonation can proceed with evolving local chemical environments, formation and disappearance of metastable phases, and evolving reactive surface areas. Together this work shows that future application of cryo-PFIB in carbonation studies would provide advanced understanding of kinetic mechanisms for optimizing industrial-scale and commercial-scale applications.

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X-ray computed tomography is generally a primary step in characterization of defective electronic components, but is generally too slow to screen large lots of components. Super-resolution imaging approaches, in which higher-resolution data is inferred from lower-resolution images, have the potential to substantially reduce collection times for data volumes accessible via x-ray computed tomography. Here we seek to advance existing two-dimensional super-resolution approaches directly to three-dimensional computed tomography data. Multiple scan resolutions over a half order of magnitude of resolution were collected for four classes of commercial electronic components to serve as training data for a deep-learning, super-resolution network. A modular python framework for three-dimensional super-resolution of computed tomography data has been developed and trained over multiple classes of electronic components. Initial training and testing demonstrate the vast promise for these approaches, which have the potential for more than an order of magnitude reduction in collection time for electronic component screening.

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.

Three-dimensional (3D) characterization provides opportunities for understanding processing-structure relationships in additively manufactured (AM) materials. Bulk samples of Inconel 718 were fabricated via electron beam melting (EBM) in order to study microstructural development as a function of energy input and beam scan strategy. TriBeam tomography of bulk Inconel 718 microstructures built under steady-state growth conditions reveals the sensitivity of microstructure formation and evolution to machine process parameters. In this study, samples manufactured using a narrow range of energy input per unit build area result in varied grain morphologies and crystallographic textures. Using TRUCHAS, a thermal simulation software, the thermal history of bulk scan strategies was predicted, and combined with a calibrated microstructure-processing map to accurately predict bulk grain morphologies. The solidification parameters and the 3D measured nucleation density are used to predict the transition between columnar and equiaxed grain morphologies, providing a process map to guide AM parameter choices to locally control as-printed microstructure. A two-dimensional metric for characterizing bulk grain morphology was also found to agree well with predictions from the process map calibrated by 3D data. Combined with 3D tomography and thermal modelling, the physics of structure development were understood at a new level of detail with respect to the competing processes of grain nucleation and epitaxial growth.

Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency and which can require frequent user interaction to be monitored and maintained. Operating upon a Robo-MET.3D™ automated mechanical serial-sectioning system, a run-to-run control algorithm has been developed to accelerate data collection and reduce data inconsistency. Using historical data amassed over a decade of experiments, a linear regression model of the deterministic system dynamics is created and used to employ a run-to-run control algorithm that optimizes selected system inputs to reduce operator intervention and increase efficacy while reducing variance of system output.

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This paper presents a run-to-run (R2R) controller for mechanical serial sectioning (MSS). MSS is a destructive material analysis process which repeatedly removes a thin layer of material and images the exposed surface. The images are then used to gain insight into the material properties and often to construct a 3-dimensional reconstruction of the material sample. Currently, an experience human operator selects the parameters of the MSS to achieve the desired thickness. The proposed R2R controller will automate this process while improving the precision of the material removal. The proposed R2R controller solves an optimization problem designed to minimize the variance of the material removal subject to achieving the expected target removal. This optimization problem was embedded in an R2R framework to provide iterative feedback for disturbance rejection and convergence to the target removal amount. Since an analytic model of the MSS system is unavailable, we adopted a data-driven approach to synthesize our R2R controller from historical data. The proposed R2R controller is demonstrated through simulations. Future work will empirically demonstrate the proposed R2R through experiments with a real MSS system.

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