TY - JOUR
T1 - Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning
AU - Chang, Tsung Wei
AU - Liao, Kai Wei
AU - Lin, Ching Chih
AU - Tsai, Mi Ching
AU - Cheng, Chung Wei
N1 - Funding Information:
This work was supported by the Ministry of Science and Technology (MOST) of Taiwan under Project MOST109-2218-E-006-034 (recipient: Mi-Ching Tsai).
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2021/6
Y1 - 2021/6
N2 - Selective laser melting (SLM) is one of the widely used metal additive manufacturing techniques. While SLM is able to produce high-quality products, the parameter selection process can be very complicated, especially for magnetic materials in that the iron loss and permeability properties must also be considered, which renders the parameter selecting process more complicated. This research explores the parameter selection process of magnetic material for SLM, which integrates machine and evolutionary algorithms to accurately predict magnetic characteristics, such as iron loss and permeability, and generates suggestions for the process parameters according to practical demands.
AB - Selective laser melting (SLM) is one of the widely used metal additive manufacturing techniques. While SLM is able to produce high-quality products, the parameter selection process can be very complicated, especially for magnetic materials in that the iron loss and permeability properties must also be considered, which renders the parameter selecting process more complicated. This research explores the parameter selection process of magnetic material for SLM, which integrates machine and evolutionary algorithms to accurately predict magnetic characteristics, such as iron loss and permeability, and generates suggestions for the process parameters according to practical demands.
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U2 - 10.1007/s00170-021-07037-y
DO - 10.1007/s00170-021-07037-y
M3 - Article
AN - SCOPUS:85104555837
SN - 0268-3768
VL - 114
SP - 3177
EP - 3184
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
IS - 9-10
ER -