Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning

Tsung Wei Chang, Kai Wei Liao, Ching Chih Lin, Mi Ching Tsai*, Chung Wei Cheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Keywords

  • Additive manufacturing
  • Evolutionary algorithm
  • Machine learning (ML)
  • Selective laser melting (SLM)
  • Soft magnetic composite (SMC)

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