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Air-coupled ultrasound detection of natural defects in wood using ferroelectret and piezoelectric sensors
Wood Science and Technology ( IF 3.4 ) Pub Date : 2020-05-28 , DOI: 10.1007/s00226-020-01189-y
M. Tiitta , V. Tiitta , M. Gaal , J. Heikkinen , R. Lappalainen , L. Tomppo

Air-coupled ultrasound was used for assessing natural defects in wood boards by through-transmission scanning measurements. Gas matrix piezoelectric (GMP) and ferroelectret (FE) transducers were studied. The study also included tests with additional bias voltage with the ferroelectret receivers. Signal analyses, analyses of the measurement dynamics and statistical analyses of the signal parameters were conducted. After the measurement series, the samples were cut from the measurement regions and the defects were analyzed visually from the cross sections. The ultrasound responses were compared with the results of the visual examination of the cross sections. With the additional bias voltage, the ferroelectret measurement showed increased signal-to-noise ratio, which is especially important for air-coupled measurement of high-attenuation materials like wood. When comparing the defect response of GMP and FE sensors, it was found that FE sensors had more sensitive dynamic range, resulting from better s / n ratio and short response pulse. Classification test was made to test the possibility of detecting defects in sound wood. Machine learning methods including decision trees, k -nearest neighbor and support vector machine were used. The classification accuracy varied between 72 and 77% in the tests. All the tested machine learning methods could be used efficiently for the classification.

中文翻译:

使用铁电驻极体和压电传感器空气耦合超声检测木材中的自然缺陷

空气耦合超声波用于通过透射扫描测量来评估木板中的自然缺陷。研究了气体矩阵压电 (GMP) 和铁电驻极体 (FE) 换能器。该研究还包括对铁电驻极体接收器进行额外偏置电压的测试。进行了信号分析、测量动态分析和信号参数的统计分析。在测量系列之后,从测量区域切下样品并从横截面目视分析缺陷。将超声响应与横截面的目视检查结果进行比较。随着额外的偏置电压,铁电驻极体测量显示信噪比增加,这对于木材等高衰减材料的空气耦合测量尤为重要。在比较 GMP 和 FE 传感器的缺陷响应时,发现 FE 传感器具有更灵敏的动态范围,这是由于更好的信噪比和更短的响应脉冲。进行分类测试以测试检测健全木材缺陷的可能性。机器学习方法包括决策树、k-最近邻和支持向量机。在测试中,分类准确率在 72% 到 77% 之间变化。所有经过测试的机器学习方法都可以有效地用于分类。进行分类测试以测试检测健全木材缺陷的可能性。机器学习方法包括决策树、k-最近邻和支持向量机。在测试中,分类准确率在 72% 到 77% 之间变化。所有经过测试的机器学习方法都可以有效地用于分类。进行分类测试以测试检测健全木材缺陷的可能性。机器学习方法包括决策树、k-最近邻和支持向量机。在测试中,分类准确率在 72% 到 77% 之间变化。所有经过测试的机器学习方法都可以有效地用于分类。
更新日期:2020-05-28
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