Index Catalog // MDR

Start Over

Filtering by: Computational methods machine learning Instrument manufacturer Zeiss Keyword Bayesian Optimization 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.

Limit your search

Type of work

Keyword

Language

Publisher

Resource type

Visibility

Rights Statement Sim

Computational methods

Data origin

Properties addressed

Synthesis and processing

Characterization methods

Material/Specimen

Author

Operator

Instrument manufacturer

Instrument model number

Search Constraints

Search Results

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.

Limit your search