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Detecting surface-breaking flaws with a Hall effect gradiometric sensor
Measurement ( IF 5.2 ) Pub Date : 2020-12-04 , DOI: 10.1016/j.measurement.2020.108808
Eloi B. de M. Junior , Fredy G. Osorio G. , Frederico V. Gutierrez , Tommaso Del Rosso , Tahir , João Gabriel G.F. Paiva , Leonardo A.F. Mendoza , Cleanio Luz-Lima , Elder Yokoyama , Andre L.A. Reis , Geronimo Perez , Jesana de M. Silva , Antonio C. Bruno , Jefferson F.D.F Araujo

Magnetic scanning devices for structures, components, and materials have been the subject of intense scientific research and are potential prototypes for use in industry, especially in non-destructive inspection, to identify defects in metal structures subject to extreme conditions such as high temperatures, pressure, and strong tension. These conditions can lead to the failure of these structures, affecting their operation and resulting in losses and possible accidents. In this sense, we built a device for scanning magnetic response in defective steel plates. The device is composed of two Hall effect sensors (Melexis MLX 90215) in a gradiometric configuration. Using this device, it was possible to identify defects produced by electric discharge machining in SAE 1020 steel plates and proved to be efficient in detecting defects with diameters and depths of the order of 0.90 mm and 100 µm, respectively. A filter system on a printed circuit board was also developed to attenuate noise. The signal-to-noise analysis showed the circuit was effective, leading to identification of defects. To reinforce the application of this equipment in scientific research and industry, we developed a theoretical model to estimate the depth of defects.



中文翻译:

使用霍尔效应梯度传感器检测表面破裂的缺陷

用于结构,部件和材料的磁扫描装置一直是科学研究的主题,并且是潜在的原型,可用于工业,尤其是在无损检测中,以识别承受极端条件(例如高温,高压)的金属结构中的缺陷,紧张感强。这些条件可能导致这些结构的故障,影响其操作并导致损失和可能的事故。从这个意义上讲,我们构建了一种用于扫描有缺陷的钢板中的磁响应的设备。该设备由两个梯度测量的霍尔效应传感器(Melexis MLX 90215)组成。使用此设备,可以识别在SAE 1020钢板上通过放电加工产生的缺陷,并被证明可以有效地检测直径和深度分别为0.90 mm和100 µm的缺陷。还开发了印刷电路板上的滤波器系统来衰减噪声。信噪比分析表明该电路有效,从而可以识别缺陷。为了加强该设备在科学研究和工业中的应用,我们开发了一种理论模型来估计缺陷的深度。

更新日期:2020-12-12
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