Tailoring magnetic hysteresis of Fe-Ni permalloy by additive manufacturing: Multiphysics-multiscale simulations of process-property relationships

TL;DR

This paper develops a comprehensive multiphysics-multiscale simulation model to understand and tailor the magnetic hysteresis of Fe-Ni permalloy produced by additive manufacturing, considering thermal, mechanical, and magnetic interactions.

Contribution

It introduces a novel simulation scheme that explicitly models coupled physical processes in AM-produced magnetic materials, linking residual stress to magnetic properties.

Findings

Residual stress significantly influences magnetic coercivity.

Simulation predicts how process parameters affect microstructure and magnetic response.

Phenomenological relation between residual stress and magnetic hysteresis established.

Abstract

Designing the microstructure of Fe-Ni permalloy by additive manufacturing (AM) opens new avenues to tailor the materials' magnetic properties. Yet, AM-produced parts suffer from spatially inhomogeneous thermal-mechanical and magnetic responses, which are less investigated in terms of process simulation and modeling schemes. Here we present a powder-resolved multiphysics-multiscale simulation scheme for describing magnetic hysteresis in materials produced via AM. The underlying physical processes are explicitly considered, including the coupled thermal-structural evolution, chemical order-disorder transitions, and associated thermo-elasto-plastic behaviors. The residual stress is identified as the key thread in connecting the physical processes and in-process phenomena across scales. By employing this scheme, we investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe21.5Ni78.5 permalloy on beam power and scan speed. Simulation results also suggest a phenomenological relation between magnetic coercivity and average residual stress, which can guide the magnetic hysteresis design of soft magnetic materials by choosing appropriate AM-process parameters.