Jonathan Raush

As the most commercially developed metal additive process, laser powder bed fusion (LPBF) is vital to advancing several industry sectors, enabling high-precision part production across aerospace, biomedical, and manufacturing industries. Al 7075 alloy offers low density and high-specific strength yet faces LPBF challenges such as hot cracking and porosity due to rapid solidification, thermal gradients, and a wide freezing range. To address these challenges, this study proposes an integrated computational and experimental framework to enhance the LPBF processability of Al 7xxx alloys by compositional modification. Using the Calculation of Phase Diagram approach, printable Al 7xxx compositions were designed by adding grain refiners (V and/or Ti) and a eutectic solidification enhancer (Mg) to Al 7075 alloy to enable grain refinement and eutectic solidification. Subsequent LPBF experiments and characterization tests, such as metallography (scanning electron microscopy), energy-dispersive X-ray spectroscopy, X-ray diffraction, and X-ray micro-computed tomography, confirmed the production of refined microstructures with reduced defects. This study contributes to existing approaches for producing high-quality Al 7xxx alloy parts without significant compositional deviations using an integrated computational and experimental approach. Finally, aligning with the Materials Genome Initiative, this study contributes to the development and industrial adoption of advanced materials.

To support computational studies and process optimization that require temperature-dependent thermophysical properties, this study characterized the wettability, surface tension, liquid–solid interfacial tension (IFT), and work of adhesion of Al 7075-T6 alloy from 923–1073 K under argon on porous alumina, tungsten, and nonporous alumina substrates using sessile drop experiments and Young’s and Young–Dupre equations, respectively. Furthermore, the substrates’ room-temperature surface free energy (SFE) characteristics were characterized using the Owens–Wendt–Rabel–Kaelble model. The contact angle results revealed the alloy’s poor wettability on all substrates. The surface tension data ranged from 718.87–942.90 mN·m⁻¹ in decreasing order of tungsten, porous alumina, and nonporous alumina. The SFE results of the porous alumina, nonporous alumina, and tungsten substrates were 44.92, 43.32, and 42.03 mN·m⁻¹, respectively. Also, the calculated liquid–solid IFT values ranged from 539.24–835.51 mN·m⁻¹ in decreasing order of porous alumina, tungsten, and nonporous alumina. Additionally, the calculated work of adhesion values ranged from 123.97–479.44 mN·m⁻¹ in decreasing order of nonporous alumina, tungsten, and porous alumina, respectively. Thus, the wettability, surface tension, and liquid–solid IFT of Al 7075-T6 alloy on the substrates were affected by the substrates’ SFE characteristics, thereby affecting the work of adhesion.

As new alloys are being developed for additive manufacturing (AM) applications, questions related to the temperature-dependent structural and compositional stability of these alloys remain. In this work, the benefits and limitations of a unique method for testing this stability are presented. This system employs the use of polychromatic synchrotron light to perform energy-dispersive x-ray diffraction (ED-XRD) on an electrostatically levitated sample at high temperatures. In comparison with a traditional angular-dispersive setup, the container-less electrostatic levitation method has unique advantages, including quicker acquisition times, simultaneous compositional information through fluorescence emissions, a reduction in background noise, and, importantly, concurrent/subsequent measurement of thermophysical properties. This combined method is ideal for phase transition studies by holding the levitated sample at a stable position and temperature through controlled heating and temperature management. To illustrate these capabilities, we show ED-XRD data of the well-known martensitic phase transition (hcp to bcc) in Ti–6Al–4V. In addition, results from the novel alloy Ni51Cu44Cr5 are presented. This alloy is shown to maintain an fcc structure upon heating. However, the concentration of Cu is reduced at high temperatures, resulting in a decrease in the lattice constant. As concurrent thermophysical properties are probed, these preliminary structure and composition experiments demonstrate the capabilities of this technique to determine the composition–processing–structure–properties of metal alloys for AM.

With the growth of 3D printing in the production space, it is inevitable that quality assurance will be needed to keep final products within the constraints of requirements. Also, the variety of materials that can be used with 3D printing has increased over the years. Testing also must consider the process of manufacturing. This paper focuses its efforts on the finished product and not the process of manufacturing. Ultrasonic testing is a type of nondestructive testing. The experiments performed in this study aim to explore the usefulness of ultrasonic testing in materials that are 3D printed. The two materials used in this study are steel alloy metals and aluminum blocks of the same dimensions—120 mm × 40 mm × 15 mm. These materials represent common choices in additive manufacturing processes. The chosen alloys, such as Aluminum (6063T6) and grade-304 stainless steel, possess distinct properties crucial for validating the proposed testing method. Metal 3D-printed materials play a pivotal role in diverse industries, since ensuring their structural integrity is imperative for reliability and safety. Testing is crucial to identify and mitigate defects that could compromise the functionality and longevity of the final products, especially in applications with demanding performance requirements. An ultrasonic transducer is used to scan for subsurface defects within the samples and an oscilloscope is used to analyze the signals. Furthermore, several Machine Learning (ML) techniques are used to estimate the severity of the defects. The application of Machine Learning methods in the manufacturing industry has proven advantageous in terms of detecting defects due to its practicality and wide application. Due to their distinct benefits in processing image information, convolutional neural networks (CNNs) are the preferred method when working with picture data. In order to perform binary and multi-class classification, support vector machines that employ the alternative kernel function are a viable option for processing sensor signals and picture data. The study reveals that ultrasonic tests are viable for metallic materials. The primary objective of this work is to evaluate and validate the application of ultrasonic testing for the inspection of 3D-printed steel alloy metals and aluminum blocks. The novelty lies in the integration of Machine Learning techniques to estimate defect severity, offering a comprehensive and non-invasive approach to quality assessment in 3D-printed materials. The proposed method can successfully detect the presence of internal defects in objects, as well as estimate the location and severity of the defects.

In this paper, small blocks of 17-4 PH stainless steel were manufactured via extrusion-based bound powder extrusion (BPE)/atomic diffusion additive manufacturing (ADAM) technology in two different orientations. Ultrasonic bending-fatigue and uniaxial tensile tests were carried out on the test specimens prepared from the AM blocks. Specifically, a recently-introduced small-size specimen design is employed to carry out time-efficient fatigue tests. Based on the results of the testing, the stress–life (S-N) curves were created in the very high-cycle fatigue (VHCF) regime. The effects of the printing orientation on the fatigue life and tensile strength were discussed, supported by fractography taken from the specimens’ fracture surfaces. The findings of the tensile test and the fatigue test revealed that vertically-oriented test specimens had lower ductility and a shorter fatigue life than their horizontally-oriented counterparts. The resulting S-N curves were also compared against existing data in the open literature. It is concluded that the large-sized pores (which originated from the extrusion process) along the track boundaries strongly affect the fatigue life and elongation of the AM parts.

The integrity of the final printed components is mostly dictated by the adhesion between the particles and phases that form upon solidification, which is a major problem in printing metallic parts using available In-Space Manufacturing (ISM) technologies based on the Fused Deposition Modeling (FDM) methodology. Understanding the melting/solidification process helps increase particle adherence and allows to produce components with greater mechanical integrity. We developed a phase-field model of solidification for binary alloys. The phase-field approach is unique in capturing the microstructure with computationally tractable costs. The developed phase-field model of solidification of binary alloys satisfies the stability conditions at all temperatures. The suggested model is tuned for Ni-Cu alloy feedstocks. We derived the Ginzburg-Landau equations governing the phase transformation kinetics and solved them analytically for the dilute solution. We calculated the concentration profile as a function of interface velocity for a one-dimensional steady-state diffuse interface neglecting elasticity and obtained the partition coefficient, k, as a function of interface velocity. Numerical simulations for the diluted solution are used to study the interface velocity as a function of undercooling for the classic sharp interface model, partitionless solidification, and thin interface.

In this paper, the phase structure, composition distribution, grain morphology, and hardness of Al6061 alloy samples made with additive friction stir deposition (AFS-D) were examined. A nearly symmetrical layer-by-layer structure was observed in the cross section (vertical with respect to the fabrication-tool traversing direction) of the as-deposited Al6061 alloy samples made with a back-and-forth AFS-D strategy. Equiaxed grains were observed in the region underneath the fabrication tool, while elongated grains were seen in the “flash region” along the mass flow direction. No clear grain size variance was discovered along the AFS-D build direction except for the last deposited layer. Grains were significantly refined from the feedstock (~163.5 µm) to as-deposited Al6061 alloy parts (~8.5 µm). The hardness of the as-fabricated Al6061 alloy was lower than those of the feedstock and their heat-treated counterparts, which was ascribed to the decreased precipitate content and enlarged precipitate size.

In this work, performance analysis and comparison of three photovoltaic technologies are carried out in the Louisiana climate. During the calendar year of 2018, the University of Louisiana at Lafayette constructed and commissioned a 1.1 MW solar photovoltaic power plant for researching solar power in southern Louisiana and for partial energy demand of the university. It was one of the largest solar photovoltaic power plants in Louisiana when constructed and receives an annual solar insolation of 4.88 kWh/m²/d at latitude minus five degrees (25°) tilt. The solar power plant has a total of 4142 modules and incorporates three module technologies. Preliminary performance data from the system level are presented. The evaluation of different technologies is based on final yield, performance ratio, and capacity factor for one year from September 2019 to August 2020. An economic analysis is carried out using levelized cost of energy for the three photovoltaic (PV) technologies. Finally, the results are compared with simulated results of System Advisor Model (SAM) and PVsyst. It was found that copper indium gallium selenide (CIGS) has better performance ratio of 0.79 compared with monocrystalline silicon and polycrystalline silicon, which have performance ratios of 0.77 and 0.73, respectively. The simulation results correlated with the actual performance of the plant.

A thermodynamic model was developed and validated to analyze a high-performance solid oxide fuel cell and gas turbine (SOFC-GT) hybrid power system for electric aviation. This study used a process simulation software package (ProMax) to study the role of SOFC design and operation on the feasibility and performance of the hybrid system. Standard modules, including compressor, turbine, heat exchanger, reforming reactor, and combustor were used from the ProMax tool suite while a custom module was created to simulate the SOFC stack. The model used an SOFC test data set as an input. Additional SOFC stack performance effects, such as pressure, temperature, and utilization of air and fuel, were added from open source data. System performance predictors were SOFC specific power, fuel-to-electricity conversion efficiency, and hybrid system efficiency. Using these input data and predictors, a static thermodynamic performance model was created that can be modified for different system configurations and operating conditions. Prior to creating the final aircraft performance model, initial demonstration models were developed to validate output results. We used the NASA SOFC model as a benchmark, which was created with their Numerical Propulsion System Simulator (NPSS) software framework. Our output results matched within 1% of both the NASA model and open source SOFC performance data. With confidence gained in the accuracy of this model, a 1-MW SOFC-GT hybrid power system was constructed for an aircraft propulsion concept. Overall hybrid system efficiencies of > 75% FTE were observed during standard 36,000 feet cruise flight conditions.