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.
Grain-scale microstructure evolution during additive manufacturing is a complex physical process. As with traditional solidification methods of material processing (e.g. casting and welding), microstructural properties are highly dependent on the solidification conditions involved. Additive manufacturing processes however, incorporate additional complexity such as remelting, and solid-state evolution caused by subsequent heat source passes and by holding the entire build at moderately high temperatures during a build. We present a three-dimensional model that simulates both solidification and solid-state evolution phenomena using stochastic Monte Carlo and Potts Monte Carlo methods. The model also incorporates a finite-difference based thermal conduction solver to create a fully integrated microstructural prediction tool. The three modeling methods and their coupling are described and demonstrated for a model study of laser powder-bed fusion of 300-series stainless steel. The investigation demonstrates a novel correlation between the mean number of remelting cycles experienced during a build, and the resulting columnar grain sizes.
The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. It is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.
Porosity in additively manufactured metals can reduce material strength and is generally undesirable. Although studies have shown relationships between process parameters and porosity, monitoring strategies for defect detection and pore formation are still needed. In this paper, instantaneous anomalous conditions are detected in-situ via pyrometry during laser powder bed fusion additive manufacturing and correlated with voids observed using post-build micro-computed tomography. Large two-color pyrometry data sets were used to estimate instantaneous temperatures, melt pool orientations and aspect ratios. Machine learning algorithms were then applied to processed pyrometry data to detect outlier images and conditions. It is shown that melt pool outliers are good predictors of voids observed post-build. With this approach, real time process monitoring can be incorporated into systems to detect defect and void formation. Alternatively, using the methodology presented here, pyrometry data can be post processed for porosity assessment.
The third Sandia Fracture Challenge (SFC3) was a benchmark problem for comparing experimental and simulated ductile deformation and failure in an additively manufactured (AM) 316L stainless steel structure. One surprising observation from the SFC3 was the Challenge-geometry specimens had low variability in global load versus displacement behavior, attributed to the large stress-concentrating geometric features dominating the global behavior, rather than the AM voids that tend to significantly influence geometries with uniform cross-sections. This current study reinvestigates the damage and failure evolution of the Challenge-geometry specimens, utilizing interrupted tensile testing with micro-computed tomography (micro-CT) scans to monitor AM void and crack growth from a virgin state through complete failure. This study did not find a correlation between global load versus displacement behavior and AM void attributes, such as void volume, location, quantity, and relative size, which incidentally corroborates the observation from the SFC3. However, this study does show that the voids affect the local behavior of damage and failure. Surface defects (i.e. large voids located on the surface, far exceeding the nominal surface roughness) that were near the primary stress concentration affected the location of crack initiation in some cases, but they did not noticeably affect the global response. The fracture surfaces were a combination of classic ductile dimples and crack deviation from a more direct path favoring intersection with AM voids. Even though the AM voids promoted crack deviation, pre-test micro-CT scan statistics of the voids did not allow for conclusive predictions of preferred crack paths. This study is a first step towards investigating the importance of voids on the ductile failure of AM structures with stress concentrations.
Madison, Jonathan D.; Andrews, Jennifer A.; Brewster, Megan B.; Clarke, Amy C.; Constant, Kristen C.; Dubon, Oscar D.; Kinser, Emily K.; Korey, Matthew K.; Larson, Natalie L.; Ochoa, Xavier O.; Rawlings, Michael R.; Rojas, Rosa M.
This SAND report fulfills the final report requirement for the Born Qualified Grand Challenge LDRD. Born Qualified was funded from FY16-FY18 with a total budget of ~$13M over the 3 years of funding. Overall 70+ staff, Post Docs, and students supported this project over its lifetime. The driver for Born Qualified was using Additive Manufacturing (AM) to change the qualification paradigm for low volume, high value, high consequence, complex parts that are common in high-risk industries such as ND, defense, energy, aerospace, and medical. AM offers the opportunity to transform design, manufacturing, and qualification with its unique capabilities. AM is a disruptive technology, allowing the capability to simultaneously create part and material while tightly controlling and monitoring the manufacturing process at the voxel level, with the inherent flexibility and agility in printing layer-by-layer. AM enables the possibility of measuring critical material and part parameters during manufacturing, thus changing the way we collect data, assess performance, and accept or qualify parts. It provides an opportunity to shift from the current iterative design-build-test qualification paradigm using traditional manufacturing processes to design-by-predictivity where requirements are addressed concurrently and rapidly. The new qualification paradigm driven by AM provides the opportunity to predict performance probabilistically, to optimally control the manufacturing process, and to implement accelerated cycles of learning. Exploiting these capabilities to realize a new uncertainty quantification-driven qualification that is rapid, flexible, and practical is the focus of this effort.
Madison, Jonathan D.; Andrew, Jennifer A.; Brewster, Megan B.; Clarke, Amy C.; Constant, Kristen C.; Dubon, Oscar D.; Kinser, Emily K.; Korey, Matthew K.; Larson, Natalie L.; Ochoa, Xavier O.; Rawlings, Michael R.; Rojas, Rosa M.
This report summarizes the data analysis activities that were performed under the Born Qualified Grand Challenge Project from 2016 - 2018. It is meant to document the characterization of additively manufactured parts and processe s for this project as well as demonstrate and identify further analyses and data science that could be done relating material processes to microstructure to properties to performance.
Additive manufacturing (AM) processes for metals can yield as-built microstructures that vary significantly from their cast or wrought counterparts. These microstructural variations can in turn, have profound effects on the properties of a component. Here, a modeling methodology is presented to investigate microstructurally-influenced mechanical response in additively manufactured structures via direct numeral simulation. Three-dimensional, synthetic voxelized microstructures are generated by kinetic Monte Carlo (kMC) additive manufacturing process simulations performed at four scan speeds to create a thin-wall cylindrical geometry notionally constructed using a concentric-pathed directed energy deposition AM process. The kMC simulations utilize a steady-state molten pool geometry that is held constant throughout the study. Resultant microstructures are mapped onto a highly-refined conformal finite-element mesh of a part geometry. A grain-scale anisotropic crystal elasticity model is then used to represent the constitutive response of each grain. The response of the structure subjected to relatively simple load conditions is studied in order to provide understanding of both the influence of AM processing on microstructure as well as the microstructure's influence on the macroscale mechanical response.
Madison, Jonathan D.; Madison, Jonathan D.; Andrew, Jennifer A.; Andrew, Jennifer A.; Brewster, Megan B.; Brewster, Megan B.; Clarke, Amy C.; Clarke, Amy C.; Constant, Kristen C.; Constant, Kristen C.; Dubon, Oscar D.; Dubon, Oscar D.; Kinser, Emily K.; Kinser, Emily K.; Korey, Matthew K.; Korey, Matthew K.; Larson, Natalie L.; Larson, Natalie L.; Ochoa, Xavier O.; Ochoa, Xavier O.; Rawlings, Michael R.; Rawlings, Michael R.; Rojas, Rosa M.; Rojas, Rosa M.
Additive manufacturing enables the rapid, cost effective production of customized structural components. To fully capitalize on the agility of additive manufacturing, it is necessary to develop complementary high-throughput materials evaluation techniques. In this study, over 1000 nominally identical tensile tests are used to explore the effect of process variability on the mechanical property distributions of a precipitation hardened stainless steel produced by a laser powder bed fusion process, also known as direct metal laser sintering or selective laser melting. With this large dataset, rare defects are revealed that affect only ≈2% of the population, stemming from a single build lot of material. The rare defects cause a substantial loss in ductility and are associated with an interconnected network of porosity. The adoption of streamlined test methods will be paramount to diagnosing and mitigating such dangerous anomalies in future structural components.
Additive manufacturing (AM) is of tremendous interest given its ability to realize complex, non-traditional geometries in engineered structural materials. However, microstructures generated from AM processes can be equally, if not more, complex than their conventionally processed counterparts. While some microstructural features observed in AM may also occur in more traditional solidification processes, the introduction of spatially and temporally mobile heat sources can result in significant microstructural heterogeneity. While grain size and shape in metal AM structures are understood to be highly dependent on both local and global temperature profiles, the exact form of this relation is not well understood. Here, an idealized molten zone and temperature-dependent grain boundary mobility are implemented in a kinetic Monte Carlo model to predict three-dimensional grain structure in additively manufactured metals. To demonstrate the flexibility of the model, synthetic microstructures are generated under conditions mimicking relatively diverse experimental results present in the literature. Simulated microstructures are then qualitatively and quantitatively compared to their experimental complements and are shown to be in good agreement.
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. Furthermore, 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.
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. Our workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. These 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. In addition, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.
An adage within the Additive Manufacturing (AM) community is that “complexity is free”. Complicated geometric features that normally drive manufacturing cost and limit design options are not typically problematic in AM. While geometric complexity is usually viewed from the perspective of part design, this advantage of AM also opens up new options in rapid, efficient material property evaluation and qualification. In the current work, an array of 100 miniature tensile bars are produced and tested for a comparable cost and in comparable time to a few conventional tensile bars. With this technique, it is possible to evaluate the stochastic nature of mechanical behavior. The current study focuses on stochastic yield strength, ultimate strength, and ductility as measured by strain at failure (elongation). However, this method can be used to capture the statistical nature of many mechanical properties including the full stress-strain constitutive response, elastic modulus, work hardening, and fracture toughness. Moreover, the technique could extend to strain-rate and temperature dependent behavior. As a proof of concept, the technique is demonstrated on a precipitation hardened stainless steel alloy, commonly known as 17-4PH, produced by two commercial AM vendors using a laser powder bed fusion process, also commonly known as selective laser melting. Using two different commercial powder bed platforms, the vendors produced material that exhibited slightly lower strength and markedly lower ductility compared to wrought sheet. Moreover, the properties were much less repeatable in the AM materials as analyzed in the context of a Weibull distribution, and the properties did not consistently meet minimum allowable requirements for the alloy as established by AMS. The diminished, stochastic properties were examined in the context of major contributing factors such as surface roughness and internal lack-of-fusion porosity. This high-throughput capability is expected to be useful for follow-on extensive parametric studies of factors that affect the statistical reliability of AM components.
Rodgers, Theron R.; Clarke, AJ C.; Gibbs, JW G.; Mertens, JCE M.; Coughlin, DR C.; Whitt, C W.; McKeown, JT M.; Roehling, JD R.; Baldwin, JK B.; Madison, Jonathan D.
The coefficient of restitution is a measure of energy dissipation in a system across impact events. Often, the dissipative qualities of a pair of impacting components are neglected during the design phase. This research looks at the effect of applying a thin layer of metallic coating, using thermal spray technologies, to significantly alter the dissipative properties of a system. The dissipative properties are studied across multiple impacts in order to assess the effects of work hardening, the change in microstructure, and the change in surface topography. The results of the experiments indicate that any work hardening-like effects are likely attributable to the crushing of asperities, and the permanent changes in the dissipative properties of the system, as measured by the coefficient of restitution, are attributable to the microstructure formed by the thermal spray coating. Further, the microstructure appears to be robust across impact events of moderate energy levels, exhibiting negligible changes across multiple impact events.
Underwood, O.; Madison, Jonathan D.; Martens, R.M.; Thompson, G.B.; Welsh, S.; Evans, J.
This study offers experimental observation of the effect of low strain conditions (ε < 10%) on abnormal grain growth (AGG) in Nickel-200. At such conditions, stored mechanical energy is low within the microstructure enabling one to observe the impact of increasing mechanical deformation on the early onset of AGG compared to a control, or nondeformed, equivalent sample. The onset of AGG was observed to occur at specific pairings of compressive strain and annealing temperature and an empirical relation describing the influence of thermal exposure and strain content was developed. The evolution of low-Σ coincident site lattice (CSL) boundaries and overall grain size distributions are quantified using electron backscatter diffraction preceding, at onset and during ensuing AGG, whereby possible mechanisms for AGG in the low strain regime are offered and discussed.
Using the kinetic Monte Carlo simulator, Stochastic Parallel PARticle Kinetic Simulator, from Sandia National Laboratories, a user routine has been developed to simulate mesoscale predictions of a grain structure near a moving heat source. Here, we demonstrate the use of this user routine to produce voxelized, synthetic, three-dimensional microstructures for electron-beam welding by comparing them with experimentally produced microstructures. When simulation input parameters are matched to experimental process parameters, qualitative and quantitative agreement for both grain size and grain morphology are achieved. The method is capable of simulating both single- and multipass welds. The simulations provide an opportunity for not only accelerated design but also the integration of simulation and experiments in design such that simulations can receive parameter bounds from experiments and, in turn, provide predictions of a resultant microstructure.
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.
Lauer, Mark A.; Poirier, David R.; Erdmann, Robert G.; Tewari, Surendra N.; Madison, Jonathan D.
This report covers the modeling of seven directionally solidified samples, five under normal gravitational conditions and two in microgravity. A model is presented to predict macrosegregation during the melting phases of samples solidified under microgravitational conditions. The results of this model are compared against two samples processed in microgravity and good agreement is found. A second model is presented that captures thermosolutal convection during directional solidification. Results for this model are compared across several experiments and quantitative comparisons are made between the model and the experimentally obtained radial macrosegregation profiles with good agreement being found. Changes in cross section were present in some samples and micrographs of these are qualitatively compared with the results of the simulations. It is found that macrosegregation patterns can be affected by changing the mold material.
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.