Jonathan D Madison

Fluid Flow and Defect Formation in the Three-Dimensional Dendritic Structure of Nickel-Based Single Crystals

Fluid flow within the dendritic structure at the solid–liquid interface in nickel-based superalloys has been studied in two directionally solidified alloy systems. Millimeter-scale, three-dimensional (3D) datasets of dendritic structure have been collected by serial sectioning, and the reconstructed mushy zones have been used as domains for fluid-flow modeling. Flow permeability and the influence of dendritic structure on flow patterns have been investigated. Permeability analyses indicate that the cross flow normal to the withdrawal direction limits the development of flow instabilities. Local Rayleigh numbers calculated using the permeabilities extracted from the 3D dataset are higher than predicted by conventional empirical calculations in the regions of the mushy zone that are prone to the onset of convective instabilities. The ability to measure dendrite surface area in 3D volumes permit improved prediction of permeability as well.

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. This workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. Methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. Additionally, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

Advancing quantitative description of porosity in autogenous laser-welds of 304L stainless steel

Porosity in linear autogenous laser welds of 304L stainless steel has been investigated using micro-computed tomography to reveal defect content in fifty-four welds made with varying delivered power, travel speed and focal lens. Trends associated with porosity size and frequencies are shown and interfacial measures are employed to provide quantitative descriptors of pore shape, directionality, interspacing and solid linear fraction. Lastly, the coefficient of variation associated with equivalent pore radii is reported toward a discussion of microstructural variability and the influence of process-parameters on such variability.

Coupling 3D Quantitative Interrogation of Weld Microstructure with 3D Models of Mechanical Response

Porosity resulting from linear autogenous laser-welds of 304L stainless steel are non-destructively examined and digitally reproduced by means of micro-computed tomography. These digitized microstructures are then imported into a finite element framework in which the pores are surrounded by an idealized, homogenized geometry, and exposed to a plastic strain-inducing failure load. Variations in equivalent plastic strain, strain at peak load and load-to-failure were all found to bear some correlation with the digitized microstructure’s local and global porosity content in simulation. Furthermore, experimental results show agreements in deformation trends predicted by simulation but reveal simulations underestimate both peak load and strain-to-failure.

Hybrid models for the simulation of microstructural evolution influenced by coupled, multiple physical processes

Most materials microstructural evolution processes progress with multiple processes occurring simultaneously. In this work, we have concentrated on the processes that are active in nuclear materials, in particular, nuclear fuels. These processes are coarsening, nucleation, differential diffusion, phase transformation, radiation-induced defect formation and swelling, often with temperature gradients present. All these couple and contribute to evolution that is unique to nuclear fuels and materials. Hybrid model that combines elements from the Potts Monte Carlo, phase-field models and others have been developed to address these multiple physical processes. These models are described and applied to several processes in this report. An important feature of the models developed are that they are coded as applications within SPPARKS, a Sandiadeveloped framework for simulation at the mesoscale of microstructural evolution processes by kinetic Monte Carlo methods. This makes these codes readily accessible and adaptable for future applications.

Porosity in millimeter-scale welds of stainless steel : three-dimensional characterization.

A variety of edge joints utilizing a continuous wave Nd:YAG laser have been produced and examined in a 304-L stainless steel to advance fundamental understanding of the linkage between processing and resultant microstructure in high-rate solidification events. Acquisition of three-dimensional reconstructions via micro-computed tomography combined with traditional metallography has allowed for qualitative and quantitative characterization of weld joints in a material system of wide use and broad applicability. The presence, variability and distribution of porosity, has been examined for average values, spatial distributions and morphology and then related back to fundamental processing parameters such as weld speed, weld power and laser focal length.

Acquisition of Real-Time Operation Analytics for an Automated Serial Sectioning System

Mechanical serial sectioning is a highly repetitive technique employed in metallography for the rendering of 3D reconstructions of microstructure. While alternate techniques such as ultrasonic detection, micro-computed tomography, and focused ion beam milling have progressed much in recent years, few alternatives provide equivalent opportunities for comparatively high resolutions over significantly sized cross-sectional areas and volumes. To that end, the introduction of automated serial sectioning systems has greatly heightened repeatability and increased data collection rates while diminishing opportunity for mishandling and other user-introduced errors. Unfortunately, even among current, state-of-the-art automated serial sectioning systems, challenges in data collection have not been fully eradicated. Therefore, this paper highlights two specific advances to assist in this area; a non-contact laser triangulation method for assessment of material removal rates and a newly developed graphical user interface providing real-time monitoring of experimental progress. Both are shown to be helpful in the rapid identification of anomalies and interruptions, while also providing comparable and less error-prone measures of removal rate over the course of these long-term, challenging, and innately destructive characterization experiments.

3D RoboMET Characterization

The goal of this project is to generate 3D microstructural data by destructive and non-destructive means and provide accompanying characterization and quantitative analysis of such data. This work is a continuing part of a larger effort to relate material performance variability to microstructural variability. That larger effort is called “Predicting Performance Margins” or PPM. In conjunction with that overarching initiative, the RoboMET.3D™ is a specific asset of Center 1800 and is an automated serialsectioning system for destructive analysis of microstructure, which is called upon to provide direct customer support to 1800 and non-1800 customers. To that end, data collection, 3d reconstruction and analysis of typical and atypical microstructures have been pursued for the purposes of qualitative and quantitative characterization with a goal toward linking microstructural defects and/or microstructural features with mechanical response. Material systems examined in FY15 include precipitation hardened 17-4 steel, laser-welds of 304L stainless steel, thermal spray coatings of 304L and geological samples of sandstone.

Proceedings of the 3rd World Congress on Integrated Computational Materials Engineering (ICME 2015): Poole/TMS

Abstract : Integrated Computational Materials Engineering (ICME) has received international attention due to its great potential to shorten product and process development time, while lowering cost and improving design outcomes. ICME is an approach to designing materials for specific applications that uses computer modeling programs to predict the behavior of materials and integrate this information into the overall materials design process. The 3rd World Congress on Integrated Computational Materials Engineering (ICME) was organized by The Minerals, Metals, and Materials Society (TMS) and held in Colorado Springs, Colorado from May 31-June 4, 2015. ONR support in the amount of

Quantitative Characterization of Porosity in Laser Welds of Stainless Steel.

15,000 was provided to support the planning, execution, and dissemination of the results of this congress.