TY - CONF A1 - Bircher, B. A1 - Meli, F. A1 - Küng, A. A1 - Bellon, Carsten A1 - Evsevleev, Sergei A1 - Katic, M. A1 - Heikkinen, V. A1 - Hemming, B. A1 - Lassila, A. T1 - Traceable determination of non-static XCT machine geometry: New developments and case studies T2 - 11th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2022) N2 - It is fundamental to determine the machine geometry accurately for dimensional X-ray computed tomography (XCT) measurements. When performing high-accuracy scans, compensation of a non-static geometry, e.g. due to rotary axis errors or drift, might become necessary. Here we provide an overview of methods to determine and account for such deviations on a per projection basis. They include characterisation of stage error motions, in situ geometry measurements, numerical simulations, and reconstruction-based optimization relying on image quality metrics and will be discussed in terms of their metrological performance. Since a radiographic calibration is always required to provide an initial absolute geometry, this method will be presented as well. The improvements of the XCT geometry correction methods are presented by means of case studies. The methods can be applied individually or in combination and are intended to provide a toolbox for XCT geometry compensation. T2 - 11th Conference on Industrial Computed Tomography (iCT 2022) CY - Online meeting DA - 08.02.2022 KW - Radiographic XCT geometry determination KW - Dimensional metrology KW - traceability KW - XCT machine geometry KW - Calibrated reference standards PY - 2022 SP - 1 EP - 10 PB - Research Group Computed Tomography, Upper Austrian University of Applied Sciences (FH OÖ) CY - Wels, Austria AN - OPUS4-54479 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Tiitta, M. A1 - Tiitta, V. A1 - Gaal, Mate A1 - Heikkinen, J. A1 - Lappalainen, R. A1 - Tomppo, L. T1 - Air-coupled ultrasound detection of natural defects in wood using ferroelectret and piezoelectric sensors JF - Wood Science and Technology N2 - 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. KW - Air-coupled transducers KW - Wood KW - Ultrasound KW - Ultrasonic imaging KW - Ferroelectret KW - Machine learning PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-509503 DO - https://doi.org/10.1007/s00226-020-01189-y SP - 1 EP - 14 PB - Springer AN - OPUS4-50950 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -