arXiv subjects

Ian McCue

Publications and source records attributed to Ian McCue.

8 recordsLinked to original sources

arXiv2026

Two-step transient liquid phase bonding of NiTi to Ti-6Al-4V through a NbZrW barrier

Dissimilar joining of NiTi to Ti-6Al-4V (Ti-64) is limited by brittle Ti2Ni formation and degradation of NiTi functionality. A two-step transient liquid phase (TLP) route was developed in which a refractory NbZrW diffusion barrier converts the incompatible couple into two independently bondable interfaces. The barrier plays a different role for each alloy: a reactive substrate for NiTi, dissolving to form a Ti-rich Ni-Ti-Nb liquid that infiltrates its own grain boundaries and is terminated by selective Ti absorption into the barrier; and an inert substrate for Ti-64, joined through contact melting of a sacrificial Cu foil. Both interfaces are fully dense and free of continuous intermetallic layers. In tension, joints spanning both interfaces began transforming at 350 MPa, exhibited a stress plateau to 2.5 global strain (consistent with stress-induced transformation of the NiTi half of the gauge) and failed beyond the plateau at 380-410 MPa. Five load-unload cycles to 460 MPa showed stable superelastic loops with minor ratcheting. These results demonstrate that a diffusion barrier enables dissimilar TLP joining of otherwise incompatible alloys.

cond-mat.mtrl-sci↗

arXiv2026

Transient Liquid Phase Bonding of NiTi Using Cu- and Nb-base Interlayers

Transient liquid phase (TLP) bonding was examined as an approach for joining NiTi to achieve a high joint efficiency while minimizing chemical variance within the joint region. Two bonding interlayer chemistries (Cu-base and Nb-base) were identified by screening thermodynamic criteria for TLP in ternary alloys using the CALPHAD method. These two systems were then experimentally evaluated with respect to their impact on solidification kinetics, microstructure in the joint region, and performance during quasistatic and cyclic tensile loading. For both interlayer chemistries, the composition profile and microstructure in the joint region confirmed an isothermal solidification mechanism. In addition, the joints were found to be fully dense and contain at most 1.2% intermetallic phases. Tensile testing showed excellent load transfer across the joints with approximately 4% recoverable strain and martensite onset stresses reaching 94% and 89% of the unbonded, annealed NiTi values for Cu-base and Nb-base interlayers, respectively. Lastly, a stable superelastic response was observed under cyclic loading for both bond chemistries, with spatial variation in the strain evolution linked to enhanced stiffness and hardness in the joint region arising from the substitutional Cu and Nb solutes, as confirmed via nanoindentation. This study demonstrates that TLP bonding of NiTi can produce high-strength and nearly intermetallic-free joints without sacrificing functional performance, such as the superelastic response.

cond-mat.mtrl-sci↗

arXiv2025

TOBACO: Topology Optimization via Band-limited Coordinate Networks for Compositionally Graded Alloys

Compositionally Graded Alloys (CGAs) offer unprecedented design flexibility by enabling spatial variations in composition; tailoring material properties to local loading conditions. This flexibility leads to components that are stronger, lighter, and more cost-effective than traditional monolithic counterparts. The fabrication of CGAs have become increasingly feasible owing to recent advancements in additive manufacturing (AM), particularly in multi-material printing and improved precision in material deposition. However, AM of CGAs requires imposition of manufacturing constraints; in particular limits on the maximum spatial gradation of composition. This paper introduces a topology optimization (TO) based framework for designing optimized CGA components with controlled compositional gradation. In particular, we represent the constrained composition distribution using a band-limited coordinate neural network. By regulating the network's bandwidth, we ensure implicit compliance with gradation limits, eliminating the need for explicit constraints. The proposed approach also benefits from the inherent advantages of TO using coordinate networks, including mesh independence, high-resolution design extraction, and end-to-end differentiability. The effectiveness of our framework is demonstrated through various elastic and thermo-elastic TO examples.

cs.CE↗

arXiv2024

Synthesis and Stability Kinetics of Nanoporous TaC Derived from Ta Precursors

Ultra-high temperature ceramics (UHTCs) are promising materials for use in next-generation aerospace structures but have primarily been used as monolithic materials or coatings due to processing limitations. Here, new functionality (e.g., ablation resistance) is introduced to these materials by developing a porous form factor that can be later infiltrated with a secondary phase. This UHTC scaffold is synthesized via gas-phase carburization of nanoporous tantalum to the ultra-high-temperature ceramic, TaC. The kinetics of Ta conversion in a carburizing environment was examined over a range of temperatures to determine rate-limiting behavior and activation energy for the process. A 1-D moving interface model was constructed to predict carburization depth and compare data from the present work to that in the literature. It was found that the activation energy for carburization increases as conversion proceeds, suggesting a transition from grain boundary to bulk diffusion. Additionally, to simulate potential use cases of this nanoporous ceramic, the compositional and morphological stability was evaluated in a high temperature environment. Finally, the utility of this thermally stable porous UHTC was demonstrated through synthesis of a nanostructured composite of TaC and oxidation-resistant material, SiO2.

cond-mat.mtrl-sci↗

arXiv2024

On-the-Fly Path Planning for the Design of Compositional Gradients in High Dimensions

Functional gradients have recently experienced an explosion in activity due to advances in manufacturing, where compositions can now be spatially varied on-the-fly during fabrication. In addition, modern computational thermodynamics has reached sufficient maturity -- with respect to property databases and the availability of commercial software -- that gradients can be designed with specific sets of properties. Despite these successes, there are practical limitations on the calculation speeds of these thermodynamic tools that make it intractable to model every element in an alloy. As a result, most path planning is carried out via surrogate models on simplified systems (e.g., approximating Inconel 718 as Ni$_{59}$Cr$_{23}$Fe$_{18}$ instead of Ni$_{53}$Cr$_{23}$Fe$_{18}$Nb$_{3}$Mo$_{2}$Ti$_{1}$). In this work, we demonstrate that this limitation can be overcome using a combination of on-the-fly sampling and a conjectured corollary of the lever rule for transformations of isothermal paths in arbitrary compositional dimensions. We quantitatively benchmark the effectiveness of this new method and find that it can be as much as 106 times more efficient than surrogate modeling.

physics.comp-ph↗

arXiv2023

Alloying Effects on the Microstructure and Properties of Laser Additively Manufactured Tungsten Materials

A large body of literature within the additive manufacturing (AM) community has focused on successfully creating stable tungsten (W) microstructures due to significant interest in its application for extreme environments. However, solidification cracking and additional embrittling features at grain boundaries have resulted in poorly performing microstructures, stymying the application of AM as a manufacturing technique for W. Several alloying strategies, such as ceramic particles and ductile elements, have emerged with the promise to eliminate solidification cracking while simultaneously enhancing stability against recrystallization. In this work, we provide new insights regarding the defects and microstructural features that result from the introduction of ZrC for grain refinement and NiFe as a ductile reinforcement phase - in addition to the resulting thermophysical and mechanical properties. ZrC is shown to promote microstructural stability with increased hardness due to the formation of ZrO2 dispersoids. Conversely, NiFe forms into micron-scale FCC phase regions within a BCC W matrix, producing enhanced toughness relative to pure AM W. A combination of these effects is realized in the WNiFe+ZrC system and demonstrates that complex chemical environments coupled with the tuning of AM microstructures provides an effective pathway for enabling laser AM W materials with enhanced stability and performance.

cond-mat.mtrl-sci↗

arXiv2023

Materials Design for Hypersonics

Hypersonic vehicles must withstand extreme conditions during flights that exceed five times the speed of sound. These systems have the potential to facilitate rapid access to space, bolster defense capabilities, and create a new paradigm for transcontinental earth-to-earth travel. However, extreme aerothermal environments create significant challenges for vehicle materials and structures. This work addresses the critical need to develop resilient refractory alloys, composites, and ceramics. We will highlight key design principles for critical vehicle areas such as primary structures, thermal protection, and propulsion systems; the role of theory and computation; and strategies for advancing laboratory-scale materials to flight-ready components.

cond-mat.mtrl-sci↗

arXiv2022

Closed-loop machine learning for discovery of novel superconductors

The discovery of novel materials drives industrial innovation, although the pace of discovery tends to be slow due to the infrequency of "Eureka!" moments. These moments are typically tangential to the original target of the experimental work: "accidental discoveries". Here we demonstrate the acceleration of intentional materials discovery - targeting material properties of interest while generalizing the search to a large materials space with machine learning (ML) methods. We demonstrate a closed-loop ML discovery process targeting novel superconducting materials, which have industrial applications ranging from quantum computing to sensors to power delivery. By closing the loop, i.e. by experimentally testing the results of the ML-generated superconductivity predictions and feeding data back into the ML model to refine, we demonstrate that success rates for superconductor discovery can be more than doubled. In four closed-loop cycles, we discovered a new superconductor in the Zr-In-Ni system, re-discovered five superconductors unknown in the training datasets, and identified two additional phase diagrams of interest for new superconducting materials. Our work demonstrates the critical role experimental feedback provides in ML-driven discovery, and provides definite evidence that such technologies can accelerate discovery even in the absence of knowledge of the underlying physics.

cond-mat.supr-con↗