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The development of encapsulation processing and a manufacturing productivity analysis for photovoltaic cells are discussed. The goals were: (1) to understand the relationships between both formulation variables and process variables; (2) to define conditions required for optimum performance; (3) to predict manufacturing yield; and (4) to provide documentation to industry.
The primary objective of this paper is to demonstrate the use of process-based manufacturing and assembly cost models in a traditional performance-focused multidisciplinary design and optimization process. The use of automated cost-performance analysis is an enabling technology that could bring realistic processbased manufacturing and assembly cost into multidisciplinary design and optimization. In this paper, we present a new methodology for incorporating process costing into a standard multidisciplinary design optimization process. Material, manufacturing processes, and assembly processes costs then could be used as the objective function for the optimization method. A case study involving forty-six different configurations of a simple wing is presented, indicating that a design based on performance criteria alone may not necessarily be the most affordable as far as manufacturing and assembly cost is concerned.
The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.
In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. Further, the performance of our proposed method is demonstrated by the extensive numerical and case studies. Note to Practitioners-Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.
Dimension accuracy, damage minimization, and defect detection are essential in manufacturing processes, especially additive manufacturing. These types of challenges may arise either during the manufacture of a product or its use. The repeatability of the process is vital in additive manufacturing systems. However, human users may lose concentration and, thus, would be a great alternative as an assistant. Depending on the nature of work, a robot’s fingers might vary, for example, mechanical, electrical, vacuum, two-fingers, and three-fingers. In addition, the end effector plays a vital role in picking up an object in the advanced manufacturing process. However, inbuilt robotic fingers may not be appropriate in different production environments. In this research presented here considering metal binder jet additive manufacturing, the two-finger end- effectors are proposed design, analysis, and experiment to pick up an object after completing the production process from a specific location. The final designs were further printed by using a 3D metal printer and installed in the existing robotic systems. These new designs are used successfully to hold the object from the specific location by reducing the contact force that was not possible with the previously installed end effector's finger. In addition, a numerical study was conducted in order to compare the flowability of the geometric shape of finger's free areas.
With the increasing concern due to climate change caused by a higher atmospheric concentration of CO 2 and other greenhouse gases, reducing environmental impact is becoming more important for every part of society. Manufacturing is responsible for a significant amount of energy/material consumption and environmental burden and, therefore, has a great opportunity to reduce its impact through green manufacturing. Green manufacturing presents opportunities across the manufacturing enterprise to increase the efficient usage of energy and material resources. These opportunities include designing products to consume fewer materials and energy during manufacturing and use, incorporating more efficient manufacturing processes, streamlining and optimizing manufacturing schedules and plans, and circularizing products. The goal of this paper will be to provide a perspective from the authors on the opportunities that exist within green manufacturing for discrete products through a review of pertinent topics and future directions. The paper will focus on processes, manufacturing equipment, manufacturing systems, recovering value at a product’s end-of-life, and additional thoughts that include metrics and indicators, techno-economic assessment, and a discussion of efficiency and effectiveness. Key findings from this review include a need for social indicators and renewable energy considerations in scheduling and process planning, integrating Industry 4.0 into circular economy along with social and institutional dimensions, consistency in the ability to measure and conceptualize metrics and indicators, a detailed evaluation of the life cycle impacts and cost of Addit Manuf, and more human and environment-oriented considerations for smart manufacturing.
The Transformational Challenge Reactor program is leveraging additive manufacturing technologies to fabricate the nuclear components required to assemble a microreactor core. Compared with traditional manufacturing processes, additive manufacturing allows for direct observation of the interior of the component during manufacturing. This unique capability promises significant possibilities for creating a new paradigm for nuclear component qualification by leveraging in-situ process data. This report describes FY21 efforts to predict material tensile properties based on data collected during the laser powder bed fusion printing process. The primary focus of this report is the test campaign designed to generate the large quantities of training data required to implement artificial intelligence algorithms that can predict these material properties. Preliminary prediction results and a demonstration of the overall data collection, analysis, and visualization pipeline are also provided.
This project provides evidence of the successful recycling of solid waste offcuts resulting from conventional manufacturing processes from three relevant alloys to next-generation nuclear reactor developers, providing a potentially upscalable circular process where no critical elements will be lost. Furthermore, iMOF-based adsorbents were successfully designed for CM extraction from aqueous solution, thereby providing a pathway for future upscaling for salvaging dissolved Ni ions. This research has achieved its goal of showing the impact of novel applications of recycling technologies for solid and liquid wastes that can be upscaled for application.
Through this collaborative effort, a new 3D printing platform for ceramics called laser-induced slip casting (LIS) was explored and developed for improving the processing and manufacture of silicon carbide (SiC), a high temperature ceramic material. This method prints layers of ceramic slips/slurries with subsequent selective-laser heating to dry each layer to build a 3D structure. The completed and dried part is then sintered. This manufacturing process is inherently lower cost than alternative ceramic printing methods such as binder jet technology or stereolithography when it comes to the feedstock material but is more expensive than robocasting or direct ink writing. However, it has the potential to make more controlled parts with less defects compared to robocasting. The largest cost is the heating source for the printer. With this technique, there is potential to make large ceramic parts in a near-net shape. Further, there is no known commercial manufacturing of 3D printed ceramics that creates a large range of different high-density ceramics at large scale. The goals and outcomes for the development of the new printing technology were: 1) assessing the technology with alumina by characterizing coupons, 2) printing and sintering of silicon carbide (SiC), and 3) characterization and properties testing of materials printed and a scale-up of SiC part(s). Through this collaborative project, it was sought to provide the best solution to achieving highly dense and near-net shaped ceramic parts at large scale and reduced cost. The goal was to produce high density sintered materials for applications in defense, energy generating systems, armor, and wear parts using 3D printing methods. Variables including powder particle sizes, dispersant molecular weights (MWs), binders, printing parameters, and post processing were explored as well as the characterization of the physical properties (add specifics here).
Supercritical CO 2 closed Brayton cycles are a major candidate for future power cycle designs in concentrating solar power applications, with high-temperature recuperators playing an essential role in realizing their high thermal efficiency. Here, printed circuit heat exchangers (PCHEs) are often chosen for this role due to their thermal-hydraulic and mechanical performance at high temperatures and pressures, all while remaining compact. However, PCHEs can be costly because of the high-performance materials demanded in these applications, and the heat exchanger internal geometry is restricted by their manufacturing process. Additively manufactured heat exchangers can address both of these shortcomings. This work proposes a modular bi-metal high-temperature recuperator with integrated headers to be produced with additive manufacturing. Beginning with existing PCHE channel geometries, a 1D heat exchanger model is developed. Then, multi-objective optimization is used to maximize the heat transfer effectiveness of a lab-scale device while limiting its size. Two distinct channel geometries emerge from the optimization. Optimal designs achieve up to 88% effectiveness with negligible pressure drop. Deterioration of effectiveness due to axial conduction of heat in the heat exchanger walls is found to be a notable problem for lab-scale PCHEs, and the optimal designs obtained here minimize its detrimental effects. A sensitivity analysis reveals that the effectiveness of the recuperator is much less sensitive to variation in mass flowrate in off-design operation when axial conduction is significant, while increasing the length of the device easily increases effectiveness.
The Manufacturing Science area of leadership spans the application of fundamental science research and development activities to understand the critical steps in manufacturing processes for the purposes of control and optimization. Although manufacturing is an applied area of research, fundamental science underpins our knowledge of both traditional and transformational manufacturing processes. For example, casting, welding, and many modern additive manufacturing processes that see wide use are broadly classified as solidification processes, with material transitioning from liquid to solid phases upon cooling from higher temperature. However, our understanding of the detailed physics involved in such processes is incomplete, and solidification remains a grand challenge for materials science. To enable adoption of transformative fabrication processes or to accelerate development of traditional ones, it is necessary to develop a detailed mechanistic understanding spanning multiple scales, starting with atomic building blocks (defects, interfaces, composition) and extending all the way up to macroscale performance in moderate and/or extreme environments.
The results of the Advanced Planar Array Development for the Space Station contract are presented. The original objectives of the contract were: (1) to develop a process for manufacturing superstrate assemblies, (2) to demonstrate superstrate technology through fabrication and test, (3) to develop and analyze a preliminary solar array wing design, and (4) to fabricate a wing segment based on wing design. The primary tasks completed were designing test modules, fabricating, and testing them. LMSC performed three tasks which included thermal cycle testing for 2000 thermal cycles, thermal balance testing at the Boeing Environmental Test Lab in Kent, Washington, and acceptance testing a 15 ft x 50 in panel segment for 100 thermal cycles. The surperstrate modules performed well during both thermal cycle testing and thermal balance testing. The successful completion of these tests demonstrate the technical feasibility of a solar array power system utilizing superstrate technology. This final report describes the major elements of this contract including the manufacturing process used to fabricate modules, the tests performed, and the results and conclusions of the tests.
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Advanced non-light water reactor designs, known as Generation IV (Gen IV) reactors, typically operate at higher temperatures and under more extreme radiation conditions than conventional light water reactors. A critical aspect of the successful deployment and advancement of Gen IV reactor designs is the selection of appropriate structural materials for specific applications, which necessitates the timely development of new materials and manufacturing processes. Advanced manufacturing (AM) offers numerous opportunities for innovative designs, enabling the production of high-performance components with potentially shorter development cycles compared to traditional manufacturing methods. However, a significant challenge in deploying AM technologies in the nuclear energy sector is the current lack of data on the irradiation performance of AM-produced components. This presentation will discuss neutron and ion irradiation results of AM materials, including stainless steel 316L, Grade 91, SA508, Inconel 718, and Inconel 625 materials. The materials were manufactured by AM, such as Powder Metallurgy Hot Isostatic Pressing (PM HIP), Laser Powder Bed Fusion (LPBF) and Directed Energy Deposition (DED). The effects of neutron irradiation on microstructure (e.g., dislocations, loops, and nanoclusters), tensile properties (e.g., strength and ductility), and ion irradiation on Irradiation-Assisted Stress Corrosion Cracking (IASCC) will be explored. Results are collected from several projects supported by the Nuclear Science User Facilities (NSUF) program.
Computational Process and Material Modeling of Powder Bed additive manufacturing of IN 718. Optimize material build parameters with reduced time and cost through modeling. Increase understanding of build properties. Increase reliability of builds. Decrease time to adoption of process for critical hardware. Potential to decrease post-build heat treatments. Conduct single-track and coupon builds at various build parameters. Record build parameter information and QM Meltpool data. Refine Applied Optimization powder bed AM process model using data. Report thermal modeling results. Conduct metallography of build samples. Calibrate STK models using metallography findings. Run STK models using AO thermal profiles and report STK modeling results. Validate modeling with additional build. Photodiode Intensity measurements highly linear with power input. Melt Pool Intensity highly correlated to Melt Pool Size. Melt Pool size and intensity increase with power. Applied Optimization will use data to develop powder bed additive manufacturing process model.
Infusible thermoplastic Elium® family of resins from Arkema have garnered much attention in recent years as a possible replacement for thermoset resins in laminate and sandwich composite manufacturing for wind blade applications due to its ease of recyclability and the ability to utilize existing manufacturing processes without imposing complicated variations. However, physical and mechanical properties of the proposed Elium® based thermoplastic composites must be comparable to existing epoxy (thermoset) based composites using manufacturing processes relevant for large wind turbine blades. A 13-meter-long demonstration blade was manufactured for that purpose and sandwich samples were obtained from that project for a detailed study. This paper details three-point flexural properties of unidirectional E-glass fiber reinforced acrylic and epoxy based sandwich panels with identical balsa wood core materials. In addition, to evaluate the relative merit considering debond failure mode, the interfacial critical strain energy release rate, predominantly in mode-1, was compared via single cantilever beam testing. In sandwich composites constructed with balsa wood core material, resin uptake by the balsa core is traditionally impeded via the insertion of a scrim material at the facesheet to core interface. Results revealed that inclusion of scrim mesh layer at the facesheet to core interface reduced flexural properties and strain energy release rates in panels infused with acrylic resin but did not significantly reduce these properties in epoxy infused facesheets.
To promote potential commercial applications of low-g technology, the materials processing and manufacturing in space program is structured to: (1) analyze the scientific principles of gravitational effects on processes used in producing materials; (2) apply the research toward the technology used to control production process (on Earth or in space, as appropriate); and (3) establish the legal and managerial framework for commercial ventures. Presently federally funded NASA research is described as well as agreements for privately funded commercial activity, and a proposed academic participation process. The future scope of the program and related capabilities using ground based facilities, aircraft, sounding rockets, and space shuttles are discussed. Areas of interest described include crystal growth; solidification of metals and alloys; containerless processing; fluids and chemical processes (including biological separation processes); and processing extraterrestrial materials.
This study will focus on the basics of generating an aluminum-tin ink that can sinter in air and exhibits properties near that of a solid aluminum-tin alloy. Sintering temperatures will also be assessed in this study. Once the optimal aluminum ink composition is determined, the optimal ink thickness for homogeneous sintering must be determined by additional experimentation. Additive manufacturing is a rapidly developing and growing manufacturing process and has proven successful in many different ways. Processes, such as extrusion three-dimensional (3D) printing and selective laser melting (SLM), have proven to work but have limitations, such as material capabilities or density issues. SLM is a revolutionary process for additive manufacturing of metals but cannot be used in outer space due to the need for metallic powder which would diffuse into the atmosphere in a zero-gravity environment. For this reason, metallic ink additive manufacturing is a potential solution. Work is being done on metallic ink additive manufacturing in a vacuum for electrical applications. This project has focused on developing an aluminum-tin metallic ink that can sinter without the need of a vacuum or inert gas-purged atmosphere in order to prevent oxidation of the aluminum by adding flux. Once a potential ink composition has been determined through sintering of small disks and thin layers of ink, the ink may be studied with a multimaterial 3D printer at NASA Marshall Space Flight Center (MSFC) in future experiments. If successful, this aluminum-tin ink will be capable for use on the International Space Station to make replacement parts quickly. Along with its zero-gravity advantages, this ink may also have applications on Earth because it may be extruded on a substrate with precise ceramic tips in a 3D printing process. This would allow the fabrication of precise, complex shapes and may generate a much faster and more efficient printing process as compared with traditional powder bed additive manufacturing processes. The process would not be limited by a small building volume because the system would not require an enclosed chamber.