Anisotropic magnetic memory signal of low-carbon steel fabricated by wire-arc directed energy deposition

IF 5.6 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Measurement Pub Date : 2025-06-15 Epub Date: 2025-03-01 DOI:10.1016/j.measurement.2025.117117
Yan Li, Sheng Bao
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Abstract

This paper investigates the anisotropic magnetic memory signal of low-carbon steel produced by wire-arc directed energy deposition (wire-arc DED) using tensile tests. Two rectangular specimens with different printing directions were tested. The residual magnetic field on the specimen surfaces during two loading stages was measured using a TSC-PC-16 magnetometer. The study reveals that material stress history significantly affects magnetic memory signals, with clear anisotropy between transversal and longitudinal specimens. The normal magnetic memory signal is less influenced by surface roughness and better reflects applied stress. Magnetic characteristic parameters are defined to quantify anisotropy and exhibit a quadratic relationship with load, enabling load level evaluation. This research highlights the potential for identifying printing direction and evaluating surface roughness in wire-arc DED components through magnetic memory signals, contributing to non-destructive testing of additive manufacturing steel.
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电弧定向能沉积低碳钢的各向异性磁记忆信号
利用拉伸试验研究了线弧定向能沉积(wire-arc directed energy deposition, DED)低碳钢的各向异性磁记忆信号。对两个不同打印方向的矩形试样进行了测试。用TSC-PC-16磁强计测量了两个加载阶段试样表面的残余磁场。研究表明,材料应力历史对磁记忆信号影响显著,横向和纵向试样具有明显的各向异性。普通磁记忆信号受表面粗糙度的影响较小,能较好地反映外加应力。定义了磁特性参数来量化各向异性,并与负载呈现二次关系,从而实现负载水平评估。这项研究强调了通过磁记忆信号识别线弧DED组件的打印方向和评估表面粗糙度的潜力,有助于增材制造钢的无损检测。
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来源期刊
Measurement
Measurement 工程技术-工程:综合
CiteScore
10.20
自引率
12.50%
发文量
1589
审稿时长
12.1 months
期刊介绍: Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.
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