Elucidating precipitation in FeCrAl alloys through explainable AI: A case study
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Materials Science↗
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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↗
Designing alloys with process-mapping AI pre-trained on empirical knowledge
<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Accelerated materials design should match the recent trends in the product development cycles. Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. However, it faces a challenge of scarce data available for training ML models. 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. Artificial Intelligence (AI) was used in this study to incorporate such knowledge in the domain-specific computational tool, pyroMind. The tool provides not only novel design ideas but also their interpretation via physics and engineering concepts.</span>
Romanov, Vyacheslav↗
The origin of passivity in aluminum-manganese solid solutions
In this work, the effects of manganese on the aqueous corrosion of aluminum-manganese alloys were investigated by experiments and atomistic simulations. Electrochemical measurements, x-ray photoelectron spectroscopy, and atom probe tomography analysis indicate that manganese addition enhanced the corrosion resistance of aluminum without participating in the surface oxidation. Furthermore, the selective dissolution of manganese was believed to increase the free volume at the metal/oxide interface to facilitate the formation of a denser, thinner oxide layer with closer to stoichiometry composition. Atomistic simulations showed that the oxide layer compactness increased, and defect density decreased with increasing free volume content in Al, resulting in enhanced barrier characteristics.
36 MATERIALS SCIENCE↗
Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys
<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>
Romanov, Vyacheslav↗
How to Feed AI: Creating a Cu-Ag Alloy Nanocrystalline Thin Films Library for Materials Informatics
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Dorman, Kyle Rhys↗
AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)
In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.
20 FOSSIL-FUELED POWER PLANTS↗
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↗
Significantly Promoting the Thermal Conductivity and Machinability of Negative Thermal Expansion Alloy via In Situ Precipitation of Copper Networks
Rapid advancements in electronic devices yield an urgent demand for high-performance electronic packaging materials with high thermal conductivity, low thermal expansion, and great mechanical properties. However, it is a great challenge for current design philosophies to fulfill all the requirements simultaneously. Here, an effective strategy is proposed for significantly promoting the thermal conductivity and machinability of negative thermal expansion alloy (Zr,Nb)Fe 2 through eutectic precipitation of copper networks. The eutectic dual-phase alloy exhibits an isotropic chips-matched thermal expansion coefficient and a thermal conductivity enhancement exceeding 200% compared with (Zr,Nb)Fe 2 , along with an ultimate compressive strength of 550 MPa. The addition of copper reorganizes the composition of (Zr,Nb)Fe 2 , which smooths the magnetic transition and shifts it toward higher temperature, resulting in linear low thermal expansion in a wide temperature range. The highly fine eutectic copper lamellae construct high thermal conductivity networks within (Zr,Nb)Fe 2 , serving as highways for heat transfer electrons and phonons. The in situ forming of eutectic copper lamellae also facilitates the mechanical properties by enhancing interfacial bonding and bearing additional stress after yielding of (Zr,Nb)Fe 2 . This work provides a novel strategy for promoting thermal conductivity and mechanical properties of negative thermal expansion alloys via eutectic precipitation of copper networks.
36 MATERIALS SCIENCE↗
Rapid solidified ductile Cu-Al-Mn ribbon and its elastocaloric potential
Abstract Cu-Al-Mn alloys display martensitic transformation over a wide range of temperatures. In addition to low cost, this alloy is known for its low transformation stress with reasonable latent heat favoring elastocaloric applications. However, the ductility of Cu-Al-Mn can be limited owing to ordering and intergranular fracture. Through rapid solidification by melt spinning, we show that Cu-Al-Mn ribbon can be made highly ductile (greater than 8% tensile strain in the as-spun state and 10% tensile strain after heat treatment). The ductility of the melt-spun ribbon is related to the suppression of L2 1 ordering that is characterized through magnetic property measurement. Heat treatment of the ribbon promotes bamboo grain formation, and the latent heat is increased to 6.4 J g −1 . Under tensile conditions, we show that the ribbon exhibited about 4 °C temperature change (4.4 °C on heating and 4.2 °C on cooling from 6.3% strain).
36 MATERIALS SCIENCE↗
Secondary Ion Mass Spectral Imaging of Metals and Alloys
Secondary Ion Mass Spectrometry (SIMS) is an outstanding technique for Mass Spectral Imaging (MSI) due to its notable advantages, including high sensitivity, selectivity, and high dynamic range. As a result, SIMS has been employed across many domains of science. In this review, we provide an in-depth overview of the fundamental principles underlying SIMS, followed by an account of the recent development of SIMS instruments. The review encompasses various applications of specific SIMS instruments, notably static SIMS with time-of-flight SIMS (ToF-SIMS) as a widely used platform and dynamic SIMS with Nano SIMS and large geometry SIMS as successful instruments. We particularly focus on SIMS utility in microanalysis and imaging of metals and alloys as materials of interest. Additionally, we discuss the challenges in big SIMS data analysis and give examples of machine leaning (ML) and Artificial Intelligence (AI) for effective MSI data analysis. Finally, we recommend the outlook of SIMS development. It is anticipated that in situ and operando SIMS has the potential to significantly enhance the investigation of metals and alloys by enabling real-time examinations of material surfaces and interfaces during dynamic transformations.
36 MATERIALS SCIENCE↗
Correlation of Fracture Behavior With Microstructure in Friction Stir Welded, and Spin Formed AI-Li 2195 Domes
Single-piece, spin-formed domes manufactured from friction stir welded (FSW) plates of Al-Li alloy 2195 have the potential to reduce the cost of fabricating cryogenic propellant tanks. Mechanical properties in the completed domes can be related directly to the final material condition and the microstructures developed. However, these new fabrication techniques have resulted in unexpected material challenges, such as abnormal grain growth in the weld nugget and the propensity for fracture in the adjacent thermo-mechanically affected zone (TMAZ). In this study, the microstructure and texture transformations within the TMAZ are related to fracture location in the vicinity of the weldment. The texture variations in the TMAZ are caused primarily by the varying amounts of shear deformation introduced during the FSW process. Grain morphology and microtexture characteristics are examined as a function of location in the TMAZ via electron backscatter diffraction (EBSD). A strong correlation between fracture location and the presence of texture banding in the TMAZ is observed. The fracture path tends to follow a distinct region of low Taylor Factor (TF) grains.
Tayon, Wesley A.↗
Evaluation of the MICAST #2-12 AI-7wt%Si Sample Directionally Solidified Aboard the International Space Station
The US team of the European led "MIcrostructure Formation in CASTing of Technical Alloys under Diffusive and Magnetically Controlled Convective Conditions" (MICAST) program recently received a third Aluminum - 7wt% silicon alloy that was processed in the microgravity environment aboard the International Space Station. The sample, designated MICAST#2-12, was directionally solidified in the Solidification with Quench Furnace (SQF) at a constant rate of 40micometers/s through an imposed temperature gradient of 31K/cm. Procedures taken to evaluate the state of the sample prior to sectioning for metallographic analysis are reviewed and rational for measuring the microstructural constituents, in particular the primary dendrite arm spacing (Lambda (sub1)), is given. The data are presented, put in context with the earlier samples, and evaluated in view of a relevant theoretical model.
Tewari, Surendra N.↗
Understanding the fast kinetics and mechanism of sodium storage in antimony using ab initio grand canonical Monte Carlo simulation and operando X-ray scattering
Using ai-GCMC simulations,operandoWAXS, and kinetics analysis, we found that the high-rate performance of Sb as an alloy anode in Na-ion batteries is due to the presence of an amorphous intermediate phase formed during sodiation and desodiation.
Chemistry↗
Powder metallurgy hot isostatic pressing - workshop report
The findings of the powder metallurgy (PM) hot isostatic pressing (HIP) workshop confirmed that the accelerated adoption of this technology requires the following action items: 1. By 2035, the HIP infrastructure must be scaled to sizes larger than 4 .0 meters for manufacturing components relevant to energy and defense industries at large. 2. The community must develop cost-effective and agile standards, qualifications, and certification pathways for these HIP parts. 3. At the same time, domestic powder production should be expanded to a wide range of metallic alloys, including Cr-Mo steels, stainless steels, and titanium- and nickel-based superalloys, as well as to emerging alloy classes in the refractory space. 4. The above initiatives can be enhanced by implementing digital modeling and controls, including AI-driven simulations, in situ monitoring, and digital twins.
36 MATERIALS SCIENCE↗
Probing rutile solid-phase crystallization of atomically mixed Mn-alloyed TiO 2 coatings through XANES analysis
Phase transformation via dopant introduction is an emerging strategy for tuning the electronic and catalytic properties of oxides. However, developing a general theory of ternary alloys and their microstructure evolution remains challenging due to the knowledge gaps in how point defects such as dopants lead to the formation of complex or metastable phases. We investigate the influence of Mn concentration and annealing temperature on the microstructure evolution of amorphous Mn-alloyed TiO 2 synthesized via Atomic Layer Deposition. In conclusion, our findings reveal a new crystallization pathway resulting in a direct amorphous TiO 2 to rutile transformation without an intermediate anatase phase and our multi-parameter thermal processing conditions show potential for AI-assisted data acquisition and analysis for studying complex oxide coatings.
36 MATERIALS SCIENCE↗
In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing
The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated in the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, we report an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.
42 ENGINEERING↗
The development of autoclave processable, thermally stable adhesives for titanium alloy and graphite composite structures
The A-type polyimide adhesive resin P11B was modified by use of mixed diamines (thio-dianiline and meta phenylene diamine) which provided the desired autoclave processability. This new resin was termed P11BA. It was shown that copolymeric blends of P11BA and Amoco AI-1137 amide-imide resin provided improved adhesive properties when autoclave processed over the properties obtained previously by press bonding with P11B based copolymeric blended adhesives. Properties of bonded assemblies are presented for long-term aging at both elevated and low temperatures, and also stress-rupture tests at elevated temperature.
Vaughan, R. W.↗