Identification of uncertainty levels of acoustic properties of biocomposites under different mounting conditions in impedance tube tests

Impedance tube method is widely used to measure acoustic properties of materials. Although this method yields reliable acoustic properties for soft textured materials, uncertainty levels of measured acoustic properties for hard materials, including biocomposites, can be quite large, mainly due to uncertain mounting conditions. Here, the effects of mounting conditions on the acoustic properties of biocomposites in an impedance tube are investigated. First, nominally identical biocomposite samples with a diameter equal to the inner diameter of impedance tube are manufactured and their acoustic properties are determined. As hard materials practically cause fitting problems in the impedance tube, the diameters of samples are reduced, as in practice, by small amounts and acoustic properties of modified samples are determined. Furthermore, in order to match the diameters of samples to the inner diameter of impedance tube, different materials such as tape, petroleum jelly and cotton are applied around samples to close the air gap between the samples and the tube's inner wall. All the results are compared, and the uncertainty levels caused by different mounting conditions on the acoustic properties of biocomposites are identified. The results show that the transmission loss (TL) measurements are dramatically affected by the mounting conditions while the sound absorption conditions are less sensitive to the mounting conditions. The deviations in the measured TL levels are highest for the samples with tape and wax (10–15 dB). On the other hand, the deviations in the measured sound absorption coefficients are highest for the samples with cotton and tape (1–2%).

Explainable machine learning: A case study on impedance tube measurements

Machine learning (ML) techniques allow for finding hidden patterns and signatures in data. Currently, these methods are gaining increased interest in engineering in general and in vibroacoustics in particular. Although ML methods are successfully applied, it is hardly understood how these black box-type methods make their decisions. Explainable machine learning aims at overcoming this issue by deepening the understanding of the decision-making process through perturbation-based model diagnosis. This paper introduces machine learning methods and reviews recent techniques for explainability and interpretability. These methods are exemplified on sound absorption coefficient spectra of one sound absorbing foam material measured in an impedance tube. Variances of the absorption coefficient measurements as a function of the specimen thickness and the operator are modeled by univariate and multivariate machine learning models. In order to identify the driving patterns, i.e. how and in which frequency regime the measurements are affected by the setup specifications, Shapley additive explanations are derived for the ML models. It is demonstrated how explaining machine learning models can be used to discover and express complicated relations in experimental data, thereby paving the way to novel knowledge discovery strategies in evidence-based modeling.

Characterization of multi-layer porous media in an impedance tube

A porous material is the combination of a solid phase and a fluid phase, with interactions and energy exchanges between phases giving rise to the dissipation of waves traveling through the porous medium. In air, mostly viscous effects and thermal effects are responsible for dissipation, in a way that strongly depends on the pore microstructure. To evaluate the intrinsic properties pertaining to this microstructure, inverse acoustic methods have been used in the past, typically using impedance tubes to observe the way a porous sample interacts with an acoustic field. The impedance tube is a widespread tool in the acoustic community and has proven to be efficient in retrieving, via an inverse method, porous material intrinsic properties such as the porosity or the tortuosity of a sample. In this work, a Bayesian representation of knowledge is taken, where information on a material property is encoded in a probability density function. When multi-layer materials are considered, classical inverse methods become ill-posed and it might become impossible to retrieve exactly each layer's intrinsic properties. This work presents two straightforward improvements that can be used in order to lift this ill-posedness and increase the precision with which material properties are obtained.

High-frequency acoustic impedance tube based on MEMS microphones

Acoustic impedance tubes are commonly used to measure a test specimen's acoustic characteristics, such as reflection factor, absorption coefficient, or acoustic impedance, in combination with one or two condenser measurement microphones according to associated standards. In the development process of an impedance tube, the microphone diaphragm's size has an important role in the measurement quality. On the one hand, the microphone diameter has to be large enough to ensure the possibility of measuring at low sound pressure levels (SPLs), but on the other hand, the size of the microphone diaphragm should be small in order not to influence the sound propagation through the impedance tube due to the microphone coupling. Micro-Electro-Mechanical Systems (MEMS) microphones are recently widely applied in various acoustic applications due to their small size and high sensitivity. This paper proposes the development of an acoustic impedance tube equipped with 16 MEMS microphones and an inner diameter of 8 mm with an operating frequency range between 60 Hz and 16 kHz. The bottom port MEMS microphones are connected via a 1 mm hole to the tube. The system evaluation is based on standard test specimens like empty probe adapters, rigid termination, and porous absorbers.

Acoustic characterization of Zea Mays culm fibers (corn) and Musa X Paradisiacal (banana) stem fibers in proportion 50% - 50%.

The following research arises as a proposal to the implementation of Zea Mays culm fibers and Musa X Paradisiaca stem fibers in proportion 50% - 50% for the development of a new eco-material with acoustic properties, the objective was to measure the absorption coefficient based on the international standard ISO 10534-1 Determination of the acoustic absorption coefficient and acoustic impedance in impedance tubes. Using the impedance tube to identify the minimums and maximums needed to perform the computational procedure in the MATLAB software tool, and finally obtain the sound absorption coefficients of the material. The measurement process is supported by the implementation of the impedance tube, having all its consideration and previous measures that support the veracity of the data taken through this process, in addition to the fact that background noise measurements were made in order to pass these values to ensure reliable results in the measurement, performed with a class 1 sound level meter; The fibers analyzed had a range between 0.8136 and 0.9225 absorption coefficient in the bands of 1000, 2000 and 4000 Hz, testing the effectiveness in their implementation as an acoustic barrier. Keywords: absorption, eco-material, fibers, barrier, acoustics.