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Ohodnicki, Jr., P. R.

Publications and source records attributed to Ohodnicki, Jr., P. R..

Nanostructure refinement and phase formation of flash annealed FeNi-based soft magnetic alloys

In this work, the resulting nanocomposite microstructures of FeNi nanocrystallites under different heating and cooling rates (5 °C/min vs 400-500 °C/s) is investigated. Conventional furnace annealing under low heating rates and slow cooling resulted in both BCC α-FeNi and FCC γ-FeNi nanocrystallites with an average grain size on the order of 25-27 nm whereas high heating rates achieved via flash annealing techniques have enabled a dramatically refined microstructure consisting of 5-7 nm grains with FCC γ-FeNi phase and found to be the dominant phase following primary crystallization. Grain size refinement and phase identity optimization yielded low values of coercivities-17 A/m and high permeability similar to 11 x 10 3 measured at 400 Hz/1 kA/m in flash annealed samples at 450 °C for 5 s. The magnetic behavior and the underlying mechanism of optimal soft magnetic properties are discussed in terms of the critical role of the grain size in domain wall pinning and coercivity.

36 MATERIALS SCIENCE↗

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗