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Filtering by: Computational methods machine learning Instrument manufacturer Zeiss Language English Publisher National Institute for Materials Science

1. Process parameters and magnetic properties (coercivity, remanence, squareness, maximum energy product) of data-driven fabrication of Nd-Fe-B anisotropic magnets by direct hot extrusion.

Description/Abstract:: We implemented an active learning pipeline assisted by machine learning and Bayesian optimization (ALMLBO) for predicting magnetic proper...
Keyword:: Active learning, Machine learning, Bayesian Optimization, Nd-Fe-B magnet, Hot extrusion, Process, Coercivity, Remanence, Squareness, and Maximum energy product
Material/Specimen:: Anisotropic permanent magnets fabricated by hot extrusion using a commercial Nd14Fe76Co3.4B6Ga0.6 (at%) powder (MQU-F™)
Resource Type:: Dataset
Data origin:: experiments
Author:: Lambard, Guillaume, Sasaki, Taisuke, Sodeyama, Keitaro, Ohkubo, Tadakatsu, and Hono, Kazuhiro
Operator:: PRYTULIAK, Anastasiia and TOYOOKA, Yoshiya
Date Uploaded:: 11/05/2021
Date Modified:: 11/10/2021

1. Process parameters and magnetic properties (coercivity, remanence, squareness, maximum energy product) of data-driven fabrication of Nd-Fe-B anisotropic magnets by direct hot extrusion.

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1. Process parameters and magnetic properties (coercivity, remanence, squareness, maximum energy product) of data-driven fabrication of Nd-Fe-B anisotropic magnets by direct hot extrusion.

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