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Filtering by: Author Lambard, Guillaume Computational methods machine learning Keyword Coercivity Keyword Hot extrusion Rights Statement Sim MIT License

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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