Additive manufactured Ti-5Al-5V-5Mo-3Cr (Ti-5553) is being considered as an AM repair material for engineering applications because of its superior strength properties compared to other titanium alloys. Here, we describe the failure mechanisms observed through computed tomography, electron backscatter diffraction (EBSD), and scanning electron microscopy (SEM) of spall damage as a result of tensile failure in as-built and annealed Ti-5553. We also investigate the phase stability in native powder, as-built and annealed Ti-5553 through diamond anvil cell (DAC) and ramp compression experiments. We then explore the effect of tensile loading on a sample containing an interface between a Ti-6Al-V4 (Ti-64) baseplate and additively manufactured Ti-5553 layer. Post-mortem materials characterization showed spallation occurred in regions of initial porosity and the interface provides a nucleation site for spall damage below the spall strength of Ti-5553. Preliminary peridynamics modeling of the dynamic experiments is described. Finally, we discuss further development of Stochastic Parallel PARticle Kinteic Simulator (SPPARKS) Monte Carlo (MC) capabilities to include the integration of alpha (α)-phase and microstructural simulations for this multiphase titanium alloy.
The present study investigated the effect of porosity surface determination methods on performance of machine learning models used to predict the tensile properties of AlSi10Mg processed by laser powder bed fusion from micro-computed tomography data. Machine learning models applied in this work include support vector machines, neural networks, decision trees, and Bayesian classifiers. The effects of isosurface thresholding and local gradient approaches for porosity segmentation, as well as image filtering schemes, on model precision were evaluated for samples produced under differing levels of global energy density.
Metal additive manufacturing allows for the fabrication of parts at the point of use as well as the manufacture of parts with complex geometries that would be difficult to manufacture via conventional methods (milling, casting, etc.). Additively manufactured parts are likely to contain internal defects due to the melt pool, powder material, and laser velocity conditions when printing. Two different types of defects were present in the CT scans of printed AlSi10Mg dogbones: spherical porosity and irregular porosity. Identification of these pores via a machine learning approach (i.e., support vector machines, convolutional neural networks, k-nearest neighbors’ classifiers) could be helpful with part qualification and inspections. The machine learning approach will aim to label the regions of porosity and label the type of porosity present. The results showed that a combination approach of Canny edge detection and a classification-based machine learning model (k-nearest neighbors or support vector machine) outperformed the convolutional neural network in segmenting and labeling different types of porosity.
Additively manufactured lattice truss structures, often referred to as architected cellular materials, present significant advantages over conventional structures due to their unique characteristics such as high strength-to-weight ratios and surface area-to-volume ratios. These geometrically complex structures, however, come with concomitant challenges for qualification and inspection. In this study, compression testing interrupted with micro-computed tomography inspection was conducted to monitor the evolution of global and local deformation throughout the loading process of 304 L stainless steel octet truss lattice structures. Both two- and three-dimensional image analysis techniques were leveraged to characterize geometric heterogeneities resulting from the laser powder bed fusion manufacturing process as well as track the structure throughout deformation. Variations from model-predicted behavior resulting from these heterogeneities are considered relative to the predicted and actual responses of the structures during compression to better understand, model, and predict the octet truss lattice structure compression response.
In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.
Laser powder bed fusion (LPBF) additive manufacturing (AM) offers a variety of advantages over traditional manufacturing, however its usefulness for manufacturing of high-performance components is currently hampered by internal defects (porosity) created during the LPBF process that have an unknown impact on global mechanical performance. By inducing porosity distributions through variations in print energy density and inspecting the resulting tensile samples using computed tomography, nearly 50,000 pores across 75 samples were identified. Porosity characteristics were quantitatively extracted from inspection data and compared with mechanical properties to understand the strength of relationships between porosity and global tensile performance. Useful porosity characteristics were identified for prediction of part performance. Results indicate that ductility and strain at ultimate tensile strength are the global tensile properties most significantly impacted by porosity and can be predicted with reasonable accuracy using simple porosity shape descriptors such as volume, diameter, and surface area. Moreover, it was found that the largest pores influenced behavior most significantly. Specifically, pores in excess of 125 µm in diameter were found to be a sufficient threshold for property estimation. These results establish an initial understanding of the complex defect-performance relationship in AM 316L stainless steel and can be leveraged to develop certification standards and improve confidence in part quality and reliability for the broader set of engineering alloys.
Architected structural metamaterials, also known as lattice, truss, or acoustic materials, provide opportunities to produce tailored effective properties that are not achievable in bulk monolithic materials. These topologies are typically designed under the assumption of uniform, isotropic base material properties taken from reference databases and without consideration for sub-optimal as-printed properties or off-nominal dimensional heterogeneities. However, manufacturing imperfections such as surface roughness are present throughout the lattices and their constituent struts create significant variability in mechanical properties and part performance. This study utilized a customized tensile bar with a gauge section consisting of five parallel struts loaded in a stretch (tensile) orientation to examine the impact of manufacturing heterogeneities on quasi-static deformation of the struts, with a focus on ultimate tensile strength and ductility. The customized tensile specimen was designed to prevent damage during handling, despite the sub-millimeter thickness of each strut, and to enable efficient, high-throughput mechanical testing. The strut tensile specimens and reference monolithic tensile bars were manufactured using a direct metal laser sintering (also known as laser powder bed fusion or selective laser melting) process in a precipitation hardened stainless steel alloy, 17-4PH, with minimum feature sizes ranging from 0.5-0.82 mm, comparable to minimum allowable dimensions for the process. Over 70 tensile stress-strain tests were performed revealing that the effective mechanical properties of the struts were highly stochastic, considerably inferior to the properties of larger as-printed reference tensile bars, and well below the minimum allowable values for the alloy. Pre- and post-test non-destructive analyses revealed that the primary source of the reduced properties and increased variability was attributable to heterogeneous surface topography with stress-concentrating contours and commensurate reduction in effective load-bearing area.
In the chemical transport field, such as petro-chemicals or food processing, there is a need to quantify the spatially varying temperature and phase state of the material within a cylindrical vessel, such as a pipeline, using non-invasive techniques. Using ultrasonic signals, which vary in time-of-flight, intensity, and wave characteristics based on the temperature and phase of a material, an automated technique is presented which can provide a non-axisymmetric map of the phase and temperature inside a cylindrical vessel within a single plane using exclusively information from the through-transmission wave and the external temperature profile. This research demonstrates the approach using an amorphous wax, due to its stable nature and ability to be reheated many times without changing the properties of the wax. Due to its amorphous nature, the wax transitions from a solid to a low-viscosity fluid over a range of temperatures. This behavior is similar to that of a thermoplastic and a slurry experiencing curing. As the spatial temperature within a container of wax increases the time of flight for an ultrasonic signal will change. Results presented indicate the ability of the investigated technique to map the temperature and phase change of the wax based solely on the ultrasonic signals and knowledge of the external temperature on the outer edge of the vessel.
Additively manufactured (AM) components often exhibit significant discontinuities and indications without a clear understanding of how they might affect the mechanical properties of a part during qualification and service. This uncertainty is unacceptable for the design and manufacturing of most aerospace components. Current research in both mechanical testing and nondestructive evaluation involves developing methods for characterizing and inspecting AM components as the use of such materials continues to rise. Although several AM manufacturing methods have been developed in recent decades, this paper focuses on AM production-ready processes for a direct metal laser sintering (DMLS) powder bed fusion machine and will provide background on Sandia National Laboratories' research efforts in this area. Tensile bar samples manufactured using the DMLS powder bed fusion method were inspected in this study, and the results of ultrasonic spectroscopy for assessing internal flaws will be presented. A combination of material property evaluation, microstructural characterization, and nondestructive inspection techniques will also be described. The results obtained from these material evaluation methods assist in determining inspection limits and methods for qualifying AM materials.
Measures of energy input and spatial energy distribution during laser powder bed fusion additive manufacturing have significant implications for the build quality of parts, specifically relating to formation of internal defects during processing. In this study, scanning electron microscopy was leveraged to investigate the effects of these distributions on the mechanical performance of parts manufactured using laser powder bed fusion as seen through the fracture surfaces resulting from uniaxial tensile testing. Variation in spatial energy density is shown to manifest in differences in defect morphology and mechanical properties. Computed tomography and scanning electron microscopy inspections revealed significant evidence of porosity acting as failure mechanisms in printed parts. These results establish an improved understanding of the effects of spatial energy distributions in laser powder bed fusion on mechanical performance.
At present, there are many methods to identify the temperature and phase of a material using invasive techniques. However, most current methods require physical contact or implicit methods utilizing light reflectance of the specimen. This work presents a nondestructive inspection method using ultrasonic wave technology that circumvents these disadvantages to identify phase change regions and infer the temperature state of a material. In the present study an experiment is performed to monitor the time of flight within a wax as it undergoes melting and the subsequent cooling. Results presented in this work show a clear relationship between a material's speed of sound and its temperature. The phase change transition of the material is clear from the time of flight results, and in the case of the investigated material, this change in the material state occurs over a range of temperatures. The range of temperatures over which the wax material melts is readily identified by speed of sound represented as a function of material temperature. The melt temperature, obtained acoustically, is validated using Differential Scanning Calorimetry (DSC), which uses shifts in heat flow rates to identify phase transition temperature ranges. The investigated ultrasonic NDE method has direct applications in many industries, including oil and gas, food and beverage, and polymer composites, in addition to many implications for future capabilities of nondestructive inspection of multi-phase materials.
Ultrasound techniques are capable of monitoring changes in the time-of-flight as a material is exposed to different thermal environments. The focus of the present study is to identify the phase of a material via ultrasound compression wave measurements in a through transmission experimental setup as the material is heated from a solid to a liquid and then allowed to re-solidify. The present work seeks to expand upon the authors' previous research, which proved this through transmission phase monitoring technique was possible, by considering different experimental geometries. The relationship between geometry, the measured speed of sound, and the temperature profile is presented. The use of different volumes helps in establishing a baseline understanding of which aspects of the experiment are geometry dependent and which are independent. The present study also investigates the relationship between the heating rate observed in the experiment and the measured speed of sound. The trends identified between the experimental geometry, heat rate and ultrasound wave speed measurement assist in providing a baseline understanding of the applicability of this technique to various industries, including the polymer industry and the oil industry.
Ultrasonic analysis is being explored as a way to capture events during melting of highly dispersive wax. Typical events include temperature changes in the material, phase transition of the material, surface flows and reformations, and void filling as the material melts. Melt tests are performed with wax to evaluate the usefulness of different signal processing algorithms in capturing event data. Several algorithm paths are being pursued. The first looks at changes in the velocity of the signal through the material. This is only appropriate when the changes from one ultrasonic signal to the next can be represented by a linear relationship, which is not always the case. The second tracks changes in the frequency content of the signal. The third algorithm tracks changes in the temporal moments of a signal over a full test. This method does not require that the changes in the signal be represented by a linear relationship, but attaching changes in the temporal moments to physical events can be difficult. This paper describes the algorithm paths applied to experimental data from ultrasonic signals as wax melts and explores different ways to display the results.
Ultrasonic analysis is being explored as a way to capture events during melting of highly dispersive wax. Typical events include temperature changes in the material, phase transition of the material, surface flows and reformations, and void filling as the material melts. Melt tests are performed with wax to evaluate the usefulness of different signal processing algorithms in capturing event data. Several algorithm paths are being pursued. The first looks at changes in the velocity of the signal through the material. This is only appropriate when the changes from one ultrasonic signal to the next can be represented by a linear relationship, which is not always the case. The second tracks changes in the frequency content of the signal. The third algorithm tracks changes in the temporal moments of a signal over a full test. This method does not require that the changes in the signal be represented by a linear relationship, but attaching changes in the temporal moments to physical events can be difficult. This study describes the algorithm paths applied to experimental data from ultrasonic signals as wax melts and explores different ways to display the results.
Ultrasonic wave propagation decreases as a material is heated. Two factors that can characterize material properties are changes in wave speed and energy loss from interactions within the media. Relatively small variations in velocity and attenuation can detect significant differences in microstructures. This paper discusses an overview of experimental techniques that document the changes within a highly attenuative material as it is either being heated or cooled from 25°C to 90°C. The experimental set-up utilizes ultrasonic probes in a through-transmission configuration. The waveforms are recorded and analyzed during thermal experiments. To complement the ultrasonic data, a Discontinuous-Galerkin Model (DGM) was also created which uses unstructured meshes and documents how waves travel in these anisotropic media. This numerical method solves particle motion travel using partial differential equations and outputs a wave trace per unit time. As a result, both experimental and analytical data are compared and presented.
Ultrasonic wave propagation decreases as a material is heated. Two factors that can characterize material properties are changes in wave speed and energy loss from interactions within the media. Relatively small variations in velocity and attenuation can detect significant differences in microstructures. This paper discusses an overview of experimental techniques that document the changes within a highly attenuative material as it is either being heated or cooled from 25°C to 90°C. The experimental set-up utilizes ultrasonic probes in a through-transmission configuration. The waveforms are recorded and analyzed during thermal experiments. To complement the ultrasonic data, a Discontinuous-Galerkin Model (DGM) was also created which uses unstructured meshes and documents how waves travel in these anisotropic media. This numerical method solves particle motion travel using partial differential equations and outputs a wave trace per unit time. Both experimental and analytical data are compared and presented.
Presented is a model verification and validation effort using low velocity impact (LVI) of carbon fiber reinforced polymer laminate experiments. The flat cylindrical indenter impacts the laminate with enough energy to produce delamination, matrix cracks and fiber breaks. Included in the experimental efforts are ultrasonic scans of the damage for qualitative validation of the models. However, the primary metrics of validation will be the force time history measured through the instrumented indenter and initial and final velocities. The simulations, which are run on in-house software, will consist of all physics and material parameters of importance as determined by a sensitivity analysis conducted on the full LVI simulation. The orthotropic damage and failure constitutive model used for the lamina is described in detail and material properties are measured, estimated from micromechanics or optimized through calibration. For all simulations of interest, the mesh and material behavior is verified through extensive convergence studies. An ensemble of simulations incorporating model parameter uncertainties is used to predict a response distribution which is then compared to experimental output. The result is a quantifiable confidence in material characterization and model physics when simulating this phenomenon in structures of interest.
The Federal Aviation Administration Airworthiness Assurance NDI Validation Center currently assesses the capability of various non-destructive inspection (NDI) methods used for analyzing aircraft components. The focus of one such exercise is to evaluate the sensitivity of fluorescent liquid penetrant inspection. A baseline procedure using the water-washable fluorescent penetrant method defines a foundation for comparing the brightness of low cycle fatigue cracks in titanium test panels. The analysis of deviations in the baseline procedure will determine an acceptable range of operation for the steps in the inspection process. The data also gives insight into the depth of each crack and which step(s) of the inspection process most affect penetrant sensitivities. A set of six low cycle fatigue cracks produced in 6.35-mm thick Ti-6Al-4V specimens was used to conduct the experiments to produce sensitivity data. The results will document the consistency of the crack readings and compare previous experiments to find the best parameters for water-washable penetrant.