Effects of processing parameters and heat treatment on the microstructure and magnetic properties of the in-situ synthesized Fe-Ni permalloy produced using direct energy deposition

Kim, Eun Seong; Haftlang, Farahnaz; Ahn, Soung Yeoul; Gu, Gang Hee; Kim, Hyoung Seop

doi:10.1016/j.jallcom.2022.164415

Cited by 22 publications

(7 citation statements)

References 45 publications

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“…The formula for transforming the target value in the present model is where represents the target value, and denotes the original target value. A of 98% is high enough to ensure that product material properties are not severely compromised 20 – 22 , so we established 98% as our criterion for high . When the , which serves as the target value, is transformed using Eq.…”

Section: Resultsmentioning

confidence: 99%

Material-agnostic machine learning approach enables high relative density in powder bed fusion products

Wang,

Jeong,

Kim

et al. 2023

Nat Commun

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This study introduces a method that is applicable across various powder materials to predict process conditions that yield a product with a relative density greater than 98% by laser powder bed fusion. We develop an XGBoost model using a dataset comprising material properties of powder and process conditions, and its output, relative density, undergoes a transformation using a sigmoid function to increase accuracy. We deeply examine the relationships between input features and the target value using Shapley additive explanations. Experimental validation with stainless steel 316 L, AlSi10Mg, and Fe60Co15Ni15Cr10 medium entropy alloy powders verifies the method’s reproducibility and transferability. This research contributes to laser powder bed fusion additive manufacturing by offering a universally applicable strategy to optimize process conditions.

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Section: Resultsmentioning

confidence: 99%

Material-agnostic machine learning approach enables high relative density in powder bed fusion products

Wang,

Jeong,

Kim

et al. 2023

Nat Commun

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“…The studies conducted by Kim et al [ 114 , 115 ] extensively demonstrate the effect of additive manufacturing parameters on the properties of these soft magnetic alloys. In [ 114 ], they conducted an evaluation of Fe–50%Ni samples manufactured using direct energy deposition based on lasers under various process conditions.…”

Section: Fe–nimentioning

confidence: 99%

“…These properties make the Fe–50%Ni alloy competitive with other soft magnetic alloys in terms of performance. In another study [ 115 ], three energy densities (4000, 4800, and 5867 J/g) based on the process parameters from the previous study were used. Subsequently, the samples were heated to 1200 °C (the temperature at which only the FCC phase exists) for 1 h and cooled in a furnace.…”

Section: Fe–nimentioning

confidence: 99%

“…characteristic allowed for be er plastic deformation buffering effects and preve instantaneous cracking. The studies conducted by Kim et al [114,115] extensively demonstrate the effe additive manufacturing parameters on the properties of these soft magnetic alloy [114], they conducted an evaluation of Fe-50%Ni samples manufactured using d energy deposition based on lasers under various process conditions. These condi include laser power ranging from 200 to 220 W, a powder feeding rate of 2.25-2.75 g/ a laser scan speed of 100-1000 mm/s, and hatch spacing of 0.06-0.3 mm.…”

Section: Fe-nimentioning

confidence: 99%

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Recent Advances in Additive Manufacturing of Soft Magnetic Materials: A Review

Vargas

Stornelli

Folgarait³

et al. 2023

Materials

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Additive manufacturing (AM) is an attractive set of processes that are being employed lately to process specific materials used in the fabrication of electrical machine components. This is because AM allows for the preservation or enhancement of their magnetic properties, which may be degraded or limited when manufactured using other traditional processes. Soft magnetic materials (SMMs), such as Fe–Si, Fe–Ni, Fe–Co, and soft magnetic composites (SMCs), are suitable materials for electrical machine additive manufacturing components due to their magnetic, thermal, mechanical, and electrical properties. In addition to these, it has been observed in the literature that other alloys, such as soft ferrites, are difficult to process due to their low magnetization and brittleness. However, thanks to additive manufacturing, it is possible to leverage their high electrical resistivity to make them alternative candidates for applications in electrical machine components. It is important to highlight the significant progress in the field of materials science, which has enabled the development of novel materials such as high-entropy alloys (HEAs). These alloys, due to their complex chemical composition, can exhibit soft magnetic properties. The aim of the present work is to provide a critical review of the state-of-the-art SMMs manufactured through different AM technologies. This review covers the influence of these technologies on microstructural changes, mechanical strengths, post-processing, and magnetic parameters such as saturation magnetization (MS), coercivity (HC), remanence (Br), relative permeability (Mr), electrical resistivity (r), and thermal conductivity (k).

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“…Due to the increasing importance of additive manufacturing (AM) technologies, the possibilities of designing soft magnetic materials by AM have been explored in a number of studies [5,6,7,8,9,10,11,12]. However, due to the delicate interplay of process conditions and resulting properties, there are several open questions that need to be answered in order to obtain AM-produced Fe-Ni permalloy parts with the desired property profile.…”

Section: Introductionmentioning

confidence: 99%

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

Yang¹,

Oyedeji²,

Zhou³

et al. 2023

Preprint

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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 thermalmechanical 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 Fe 21.5 Ni 78.5 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.

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Effects of processing parameters and heat treatment on the microstructure and magnetic properties of the in-situ synthesized Fe-Ni permalloy produced using direct energy deposition

Cited by 22 publications

References 45 publications

Material-agnostic machine learning approach enables high relative density in powder bed fusion products

Material-agnostic machine learning approach enables high relative density in powder bed fusion products

Recent Advances in Additive Manufacturing of Soft Magnetic Materials: A Review

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

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