Linking materials systems approach to alloy design and part qualification for laser-powder bed fusion additive manufacturing
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Thapliyal, Saket↗
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Results for “Alloy design”
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A defect-resistant Co–Ni superalloy for 3D printing
Abstract Additive manufacturing promises a major transformation of the production of high economic value metallic materials, enabling innovative, geometrically complex designs with minimal material waste. The overarching challenge is to design alloys that are compatible with the unique additive processing conditions while maintaining material properties sufficient for the challenging environments encountered in energy, space, and nuclear applications. Here we describe a class of high strength, defect-resistant 3D printable superalloys containing approximately equal parts of Co and Ni along with Al, Cr, Ta and W that possess strengths in excess of 1.1 GPa in as-printed and post-processed forms and tensile ductilities of greater than 13% at room temperature. These alloys are amenable to crack-free 3D printing via electron beam melting (EBM) with preheat as well as selective laser melting (SLM) with limited preheat. Alloy design principles are described along with the structure and properties of EBM and SLM CoNi-base materials.
36 MATERIALS SCIENCE↗
Superalloy Disk With Dual-Grain Structure Spin Tested
Advanced nickel-base disk alloys for future gas turbine engines will require greater temperature capability than current alloys, but they must also continue to deliver safe, reliable operation. An advanced, nickel-base disk alloy, designated Alloy 10, was selected for evaluation in NASA s Ultra Safe Propulsion Project. Early studies on small test specimens showed that heat treatments that produced a fine grain microstructure promoted high strength and long fatigue life in the bore of a disk, whereas heat treatments that produced a coarse grain microstructure promoted optimal creep and crack growth resistance in the rim of a disk. On the basis of these results, the optimal combination of performance and safety might be achieved by utilizing a heat-treatment technology that could produce a fine grain bore and coarse grain rim in a nickel-base disk. Alloy 10 disks that were given a dual microstructure heat treatment (DMHT) were obtained from NASA s Ultra-Efficient Engine Technology (UEET) Program for preliminary evaluation. Data on small test specimens machined from a DMHT disk were encouraging. However, the benefit of the dual grain structure on the performance and reliability of the entire disk still needed to be demonstrated. For this reason, a high temperature spin test of a DMHT disk was run at 20 000 rpm and 1500 F at the Balancing Company of Dayton, Ohio, under the direction of NASA Glenn Research Center personnel. The results of that test showed that the DMHT disk exhibited significantly lower crack growth than a disk with a fine grain microstructure. In addition, the results of these tests could be accurately predicted using a two-dimensional, axisymmetric finite element analysis of the DMHT disk. Although the first spin test demonstrated a significant performance advantage associated with the DMHT technology, a second spin test on the DMHT disk was run to determine burst margin. The disk burst in the web at a very high speed, over 39 000 rpm, in line with the predicted location and speed. Furthermore, significant growth of the disk was observed before failure, in line with predictions, clearly demonstrating the reliability and safety of the DMHT technology. Although successful spin testing in Ultra Safe's Nickel Disk Program represents a significant milestone for DMHT technology, realistic engine operation will require repeated loading of a DMHT nickel disk. For this reason, a cyclic spin test study of DMHT nickel disk technology has been proposed to start in fiscal year 2003 under NASA's Aviation Safety Program. The goal of this program will be to determine the fatigue failure mechanism in DMHT nickel disks, thereby demonstrating the reliability and safety of DMHT technology under repetitive loading conditions encountered in realistic engine operation.
Gayda, John↗
Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing
Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.
Wang, Xin↗
DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys
Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.
AI/ML↗
Evaluating the intrinsic resistance to balling of alloys: A High-throughput physics-informed and data-enabled approach
To date, the vast majority of work on metal additive manufacturing (AM) has been framed in terms of the need to tune processing conditions for a particular AM technology in order to print conventional alloys, oftentimes developed for fabrication methods other than AM. This approach overlooks the fact that historically, many engineering alloy system has been designed with a particular processing route in mind, e.g., ingot metallurgy, powder metallurgy, rapid quenching, etc. There are thus significant opportunities to design alloys specifically for AM. A key challenge is that alloy design requires performance metrics that can be optimized by exploring the alloy chemistry space. Here, we present a study in which we examine how intrinsic thermophysical properties can be used to estimate performance metrics related to the behavior of a solidifying metal droplet under AM-relevant conditions. By identifying these intrinsic properties, it is possible to directly incorporate ‘intrinsic printability’, specifically ‘intrinsic resistance to balling’, into AM-focused alloy design.
20 FOSSIL-FUELED POWER PLANTS↗
Physics-informed, empirically constrained machine learning for designing Fe-9Cr alloys
<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. It was demonstrated that the domain knowledge-based empirical constraints not only inhibit overfitting but also allow training more accurate and reliable ML models with improved transparency of the output interpretation. In this study, alloy tensile properties were interpreted with three competing virtual-microstructure models.</span>
Romanov, Vyacheslav↗
Design of heterogeneous structured Al alloys with wide processing window for laser-powder bed fusion additive manufacturing
Required microstructural attributes of an alloy vary with structural applications. The microstructural fine-tuning capability of laser-powder bed fusion (L-PBF) additive manufacturing (AM) enables application specific manufacture of the components. Such manufacture with L-PBF AM requires alloys that exhibit wide processing window and are amenable to multiple deformation mechanisms. However, high hot cracking susceptibility of Al alloys poses a barrier to such printability-performance synergy. In this work we show that an integration of, a) grain refinement through heterogeneous nucleation, and b) eutectic solidification, leads to crack-free parts at wide range of process parameters, microstructural heterogeneity, and hierarchy in the Al-Ni-Ti-Zr alloy. Such an integration targets hot cracking at multiple stages of solidification in L-PBF as opposed to the contemporary alloy design strategies that target hot-cracking at only specific stages of solidification. The Al-Ni-Ti-Zr alloy exhibits excellent printability and a high as-built tensile performance. Due to the wide processing window and amenability to multiple deformation mechanisms, the alloy microstructure and subsequently the performance, can be fine-tuned. Here, such strategy opens the gateway for application-specific manufacture of Al alloys with L-PBF AM and establishes a fundamental shift in current methodologies for design of these alloys for L-PBF AM.
36 MATERIALS SCIENCE↗
Guided Design of Alloys
Using multiscale materials modeling, we are designing stronger alloys for the airspace applications. We focus on improving mechanical properties of materials at operating temperature. We employ basic science and fundamental theory for guided design of the next-generation alloys. Among the examples, we consider precipitated superalloys with a local phase transformation strengthening. We acknowledge funding by NASA’s Aeronautics Research Mission Directorate (ARMD) via Transformational Tools and Technologies (TTT) Project.
multiscale↗
Towards the holistic design of alloys with large language models
Large language models are very effective at solving general tasks, but can also be useful in materials design and extracting and using information from the scientific literature and unstructured corpora. For instance, in the domain of alloy design and manufacturing, they can expedite the materials design process and enable the inclusion of holistic criteria.
36 MATERIALS SCIENCE↗
Rapid Design of Cost-Effective Refractory High Entropy Alloys Strengthened by Precipitation
Funded by the ARPA-E ULTIMATE program, this project aims to develop cost-effective refractory high entropy alloys (RHEAs) for gas turbine blades applications at 1300 degree Celsius and above. The alloy design focuses on carbides precipitation strengthening in a strong and ductile solid solution BCC matrix. The goal is to achieve balanced mechanical properties including room temperature ductility & fracture toughness and high temperature strength and creep resistance, while maintaining comparable oxidation resistance and densities to Ni-base superalloys. To accelerate alloy design, multiscale modeling including CALPHAD and first-principles density functional theory (DFT) calculations and machine learning are performed. Specifically, high throughput phase diagram calculations and screening are carried out using CALPHAD; DFT calculations are performed to predict intrinsic ductility, grain boundary strengthening potential, coefficient of thermal expansion (CTE) and temperature-dependent elastic constants. Temperature dependent yield stress, creep strain and CTE are also predicted using machine learning. Down-selected alloys are synthesized in small buttons of about 250g using arc melting for rapid evaluation on microstructure and mechanical properties before employing plasma arc melting and additive manufacturing for producing large ingots. Preliminary computational and experimental results of this project will be presented and future direction in RHEA development will be discussed.
high temperature materials↗
Guided Design of Alloys: Part 2
Using multiscale materials modeling, we are designing stronger alloys for the airspace applications. We focus on improving mechanical properties of materials at operating temperature. We employ basic science and fundamental theory for guided design of the next-generation alloys. Among the examples, we consider precipitated superalloys with a local phase transformation strengthening. We acknowledge funding by NASA’s Aeronautics Research Mission Directorate (ARMD) via Transformational Tools and Technologies (TTT) Project.
Multiscale↗
Shape Memory Alloy Actuator Design: CASMART Collaborative Best Practices
Upon examination of shape memory alloy (SMA) actuation designs, there are many considerations and methodologies that are common to them all. A goal of CASMART's design working group is to compile the collective experiences of CASMART's member organizations into a single medium that engineers can then use to make the best decisions regarding SMA system design. In this paper, a review of recent work toward this goal is presented, spanning a wide range of design aspects including evaluation, properties, testing, modeling, alloy selection, fabrication, actuator processing, design optimization, controls, and system integration. We have documented each aspect, based on our collective experiences, so that the design engineer may access the tools and information needed to successfully design and develop SMA systems. Through comparison of several case studies, it is shown that there is not an obvious single, linear route a designer can adopt to navigate the path of concept to product. SMA engineering aspects will have different priorities and emphasis for different applications.
Benafan, Othmane↗
Mechanical fastening of titanium and its alloys
Designing, forming, machining, and assembling for joining titanium and titanium alloys with mechanical fasteners
MACHINING↗
Bayesian optimization with active learning of design constraints using an entropy-based approach
Abstract The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.
36 MATERIALS SCIENCE↗
Investigating Spillover Energy as a Descriptor for Single-Atom Alloy Catalyst Design
The identification of thermodynamic descriptors of catalytic performance is essential for the rational design of heterogeneous catalysts. Here, we investigate how spillover energy, a descriptor quantifying whether intermediates are more stable at the dopant or host metal sites, can be used to design single-atom alloys (SAAs) for formic acid dehydrogenation. Further, using theoretical calculations, we identify NiCu as a SAA with favorable spillover energy and demonstrate that formate intermediates produced after the initial O–H activation are more stable at Ni sites where rate-determining C–H activation occurs. Surface science experiments demonstrated that NiCu(111) SAAs are more reactive than Cu(111) while they still follow the formate reaction pathway. However, reactor studies of silica-supported NiCu SAA nanoparticles showed only a modest improvement over Cu resulting from surface coverage effects. Overall, this study demonstrates the potential of engineering SAAs using spillover energy as a design parameter and highlights the importance of adsorbate–adsorbate interactions under steady-state operation.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Solid Phase Gradient Alloying Method via ShAPE
Alloying has been used to confer desirable properties to various metallic systems since the bronze age. For example, Ni, Mn, and Cr are alloyed into an Fe matrix to make steel, which has improved strength and corrosion resistance (as displayed in Fig. 1). In addition to the type of alloying elements, the amount of alloying elements added into a matrix is also very critical for a material’s mechanical property, physical property, machinability, and cost. Conventionally, new alloys are discovered by combining alloy precursors and elemental constituents into a mixture and melting them together. The solidified product or ingot represents a usually non-homogeneous combination of the chemistry with a microstructure dictated by the physics of solidification. It is time and energy intensive to optimize the alloying amounts via the casting process and hence, economically not conducive to rapid alloy discovery. In addition, the cast microstructure seen in the as-cast ingot is often not the ideal, nor even desired structure for optimized performance. Cast materials often need further processing such as homogenization, annealing, and deformation work put in through rolling, forging, extruding, etc. to have favorable microstructures and properties. A faster method to discover new alloy combinations and evaluate them in their worked or processed form is needed. Recently, Laser Engineered Net Shaping was applied to fabricate gradient compositional material for alloy designing. However, the mechanical properties of this gradient alloy were poor due to the existence of impurities, oxidation and cracks that are inherent in the melt-solidification process. Because of these limitations, we propose to use a solid-phase process (ShAPE) to create bulk materials (extrudates) that vary in composition from one end of the extruded solid to the other. Various manufacturing methods for alloy design including conventional casting method, laser based combinatorial method and current solid phase gradient alloying method are displayed and compared in Fig. 2. ShAPE machine and a schematic of ShAPE process are displayed in Fig. 3. Starting material is processed by a rotating and plunging die to form an extrudate. We are proposing through this methodology to invent a new, bulk-scale combinatorial technique. With this novel technique, alloying element can be dissolved into the matrix with a continued gradient without bulk melting. Formation of inter-metallics and defects originating from melt processing can be avoided. In addition, the ShAPE process creates the mixed alloy chemistry, and meanwhile subjects the material to severe plastic strain, which induces the favorable “worked” microstructure. Products from the ShAPE process can be tested in hardness directly providing a path to rapid evaluation of mechanical properties. The ability to create graded structures using friction stir processing (FSP) has been demonstrated, however using the ShAPE process we believe will lead to much faster and higher fidelity results. When combined with high throughput screening methods of physical, mechanical and microstructural property characterization, efficiency and accuracy of alloy design can be significantly improved.
36 MATERIALS SCIENCE↗
Diffusional lithium trapping as a failure mechanism of aluminum foil anodes in lithium-ion batteries
Aluminum foils are an appealing anode for lithium-ion batteries due to high capacity and low-cost, but their viability has been limited due to poor cyclability arising from pulverization and solid-electrolyte interphase growth. Here, we show that significant capacity degradation of aluminum foil anodes during electrochemical cycling also occurs due to diffusional lithium trapping. Scanning electron microscopy of cross-sectioned, cycled foils in the delithiated state reveals large regions of β-LiAl that are passivated by a surface layer of α-Al, which has poor Li + diffusivity. It is found that lithium diffusion occurs preferentially along the β-LiAl grain boundaries, so the grain structure after initial lithiation significantly affects the trapping behavior. Diffusional lithium trapping is exacerbated by both higher delithiation rates and higher areal capacity, presenting a challenge towards commercialization of aluminum foil anodes. We further demonstrate that diffusional trapping in aluminum foil anodes can be mitigated through alloy design, with the addition of 2–3 wt% Li yielding improved first cycle efficiency, and the addition of 1 wt% Si yielding improved cycle life. These results provide a mechanistic understanding of diffusional lithium trapping in aluminum foil anodes and highlight compositional design of alloys as a promising strategy to overcome it.
25 ENERGY STORAGE↗