通过振动来辨别不同的材料

Tommaso Lisini Baldi, S. Marullo, N. D’Aurizio, D. Prattichizzo
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引用次数: 0

摘要

从机械工程到医疗和工业部门,材料表征和鉴别对多种应用都很感兴趣。尽管需要自动化系统,但现有的大多数方法都需要昂贵且笨重的硬件,这些硬件不能在特定实验室之外使用。在这项工作中,我们提出了一种通过振动和机器学习技术主动刺激来区分不同材料和检测材料内部变化的新技术。音圈致动器和三轴加速度计用于产生和采样通过材料传播的机械振动。分析结果证实了所提方法的有效性。利用神经网络处理在材料中传播的机械振动信号是一种可行的材料分类方法。这不仅适用于区分具有明显差异的材料,也适用于检测材料是否在其结构上经历了一些细微的变化。此外,500赫兹的机械振动显示了提供紧凑而有意义的数据表示的能力,足以对8种不同的材料进行分类,并将参考材料与其他有缺陷的材料区分开来,平均精度大于90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discriminating different materials by means of vibrations
Material characterization and discrimination is of interest for multiple applications, ranging from mechanical engineering to medical and industrial sectors. Despite the need for automated systems, the majority of the existing approaches necessitate expensive and bulky hardware that cannot be used outside ad-hoc laboratories. In this work, we propose a novel technique for discriminating between different materials and detecting intra-material variations using active stimulation through vibration and machine learning techniques. A voice-coil actuator and a tri-axial accelerometer are used for generating and sampling mechanical vibration propagated through the materials. Results of the present analysis confirm the effectiveness of the proposed approach. Processing a mechanical vibration signal that propagates through a material by means of a neural network is a viable means for material classification. This holds not only for distinguishing materials having gross differences, but also for detecting whether a material underwent some slight changes in its structure. In addition, mechanical vibrations at 500 Hz demonstrated an ability to provide a compact and meaningful representation of the data, sufficient to categorize 8 different materials, and to distinguish reference materials from other defective materials, with an average accuracy greater than 90%.
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