Othmane Benafan | Searcharxiv

arXiv2024

Manufacturing, processing, applications, and advancements of Fe-based shape memory alloys

Fe-based shape memory alloys (Fe-SMAs) belong to smart metallic materials that can memorize or restore their preset shape after experiencing a substantial amount of deformation under heat, stress, or magnetic stimuli. Fe-SMAs have remarkable thermomechanical properties and have attracted significant interest because of their potential merits, such as cost-effective alloying elements, superior workability, weldability, a stable superelastic response, and low-temperature dependence of critical stress required for stress-induced martensitic transformation. Therefore, Fe-SMAs can be an intriguing and economical alternative to other SMAs. The recent advancements in fabrication methods of conventional metals and SMAs are helping the production of customized powder composition and then customized geometries by additive manufacturing (AM). The technology in these areas, i.e., fabrication techniques, experimental characterization, and theoretical formulations of Fe-SMAs for conventional and AM has been rapidly advancing and is lacking a comprehensive review. This paper provides a critical review of the recent developments in Fe-SMAs-related research. The conventional and AM-based methods of producing Fe-SMAs are discussed, and a detailed review of the current research trends on Fe-SMAs including 4-D printing of Fe-SMAs are comprehensively documented. The presented review provides a comprehensive review of experimental methods and processes used to determine the material characteristics and features of Fe-SMAs. In addition, the work provides a review of the reported computational modeling of Fe-SMAs to help design new Fe-SMA composition and geometry. Finally, different Fe-SMAs-based applications such as sensing and damping systems, tube coupling, and reinforced concrete are also discussed.

physics.app-ph↗

arXiv2020

Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration

Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A physics-informed featured engineering approach is shown to enable otherwise poorly performing ML models to perform well with the same data. Specifically, previously engineered elemental features based on alloy chemistries are combined with newly engineered heat treatment process features. The new features result from first transforming the heat treatment parameter data as it was previously recorded using nonlinear mathematical relationships known to describe the thermodynamics and kinetics of phase transformations in alloys. The ability of the ML model to be used for predictive design is validated using blind predictions. Composition - process - property relationships for thermal hysteresis of shape memory alloys (SMAs) with complex microstructures created via multiple melting-homogenization-solutionization-precipitation processing stage variations are captured, in addition to the mean transformation temperatures of the SMAs. The quantitative models of hysteresis exhibited by such highly processed alloys demonstrate the ability for ML models to design for physical complexities that have challenged physics-based modeling approaches for decades.

cond-mat.mtrl-sci↗