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Thermomechanical loading paths involving a simultaneous increase of stress and decrease of temperature (i.e., out-of-phase paths) were investigated for a NiTiHf High-Temperature Shape Memory Alloy (HTSMA). Isothermal and isobaric loadings were first performed to characterize the fundamental shape memory properties and establish the stress-temperature phase diagram. Fully-transforming out-of-phase loadings were then performed for different maximum stress levels. The obtained mechanical responses exhibited significant recoverable strains, indicating reversible martensitic transformations, contrary to the mechanical responses under pure isothermal mechanical loading. The out-of-phase responses were compared to those under isobaric paths to analyze the phase-transformation characteristics and identify the role of loading paths on the transformation reversibility and the possible interactions between deformation modes. The out-of-phase paths produce strain responses similar to the ones obtained from isobaric actuation tests. However, the strain recovery can be observed from both strain-temperature and stress–strain perspectives. Since recovery can occur from a stress–strain perspective, it is denominated as ”non-isothermal superelasticity”. The transformation temperatures obtained for these paths showed similar values to the ones corresponding to isobaric loading. A general definition of the work output is proposed to capture it under varying stresses, as opposed to the classical definition under constant stress levels in isobaric actuation experiments. An analysis of the work inputs and outputs, using this new definition, revealed that out-of-phase loadings produce a lower but relatively constant work output as a function of the stress level for a significantly lower work input, enabling new possibilities for HTSMA actuators in environments with limited work input available. 

Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces. 

BALPI accelerates phase diagram discovery by adaptively sampling thermodynamic space with quantified uncertainty, efficiently identifying sparse and disconnected phase stability regions. 

The martensitic transformation in NiTi-based Shape Memory Alloys (SMAs) provides a basis for shape memory effect and superelasticity, thereby enabling applications requiring solid-state actuation and large recoverable shape changes upon mechanical load cycling. In order to tailor the transformation to a particular application, the compositional dependence of properties in NiTi-based SMAs, such as martensitic transformation temperatures and hysteresis, has been exploited. However, the compositional design space is large and complex, and experimental studies are expensive. In this work, we develop an interpretable piecewise linear regression model that predicts the parameter, a measure of compatibility between austenite and martensite phases, and an (indirect) factor that is well-correlated with martensitic transformation hysteresis, based on the chemical features derived from the alloy composition. The model is capable of predicting, for the first time, the type of martensitic transformation for a given alloy chemistry. The proposed model is validated by experimental data from the literature as well as in-house measurements. The results show that the model can effectively distinguish between B19 and regions for any given composition in NiTi-based SMAs and accurately estimate the lambda_2 parameter. 

The present work investigates fracture toughness, and actuation and mechanical fatigue crack growth responses of Ni50.3Ti29.7Hf20 HTSMAs across martensitic transformation with two different microstructures, one with H-phase nanoprecipitates and one without. H-phase precipitation is known to stabilize the actuation cycling response of NiTiHf HTSMAs and notably impacts transformation-induced plasticity. The fracture toughness tests performed reveal that precipitate-free NiTiHf has a higher fracture toughness and undergoes significantly more inelastic deformation than the one with the precipitates resulting in toughness enhancement, i.e., stable crack advance during fracture toughness experiments, which is not observed in the precipitated NiTiHf for the crack configuration and loading conditions tested. Furthermore, the precipitate free NiTiHf has higher actuation and mechanical fatigue crack growth resistance than the precipitation-hardened microstructure. This is attributed to plasticity buildup, which exacerbates the manifestation of retained martensite upon repeated transformations. The fatigue crack growth rates obtained from both actuation and mechanical fatigue experiments align to a single Paris Law Curve for the precipitation-hardened NiTiHf. This work aims to determine if unified Paris Law curves can be generated from mechanical and actuation fatigue experiments, irrespective of composition and microstructure, to estimate actuation fatigue crack growth rates, laborious and challenging to measure, from easier to detect mechanical fatigue crack growth rates. 

Refractory high entropy alloys (RHEAs) have gained significant attention in recent years as potential replacements for Ni-based superalloys in gas turbine applications. Improving their properties, such as their high-temperature yield strength, is crucial to their success. Unfortunately, exploring this vast chemical space using exclusively experimental approaches is impractical due to the considerable cost of the synthesis, processing, and testing of candidate alloys, particularly at operation-relevant temperatures. On the other hand, the lack of reasonably accurate predictive property models, especially for high-temperature properties, makes traditional Integrated Computational Materials Engineering (ICME) methods inadequate. In this paper, we address this challenge by combining machine-learning models, easy-to-implement physics-based models, and inexpensive proxy experimental data to develop robust and fast-acting models using the concept of Bayesian updating. The framework combines data from one of the most comprehensive databases on RHEAs (Borg et al., 2020) with one of the most widely used physics-based strength models for BCC-based RHEAs (Maresca and Curtin, 2020) into a compact predictive model that is significantly more accurate than the state-of-the-art. This model is cross-validated, tested for physics-informed extrapolation, and rigorously benchmarked against standard Gaussian process regressors (GPRs) in a toy Bayesian optimization problem. Such a model can be used as a tool within ICME frameworks to screen for RHEAs with superior high-temperature properties. The code associated with this work is available at: https://codeocean.com/capsule/7849853/tree/v2.