Helmholtz Resonances in Photoacoustic Experiment with Laser-Sintered Polyamide Including Thermal Memory of Samples

Popović, Miroslav; Nesic, M. V.; Ćirić‐Kostić, Snežana; Živanov, M.; Markushev, D. D.; Rabasović, Mihailo D.; Galović, S.

doi:10.1007/s10765-016-2124-3

Cited by 17 publications

(5 citation statements)

References 21 publications

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“…The influence of the used instruments is reflected in the fact that the amplitude of the photoacoustic signal of the sample is distorted in the low and high frequency parts, and the phase shifts its position, as is shown in Figure 2 . With the developed methodology of removing the instrumental influence [ 35 , 36 , 37 , 38 , 39 , 40 ], from the microphone to the accompanying electronics, it was shown that it is possible from the recorded photoacoustic response S ( f ) to obtain the photoacoustic signal δp total ( f ), with a wide frequency range of 20 to 20 kHz, which can be used for further precise characterization [ 36 , 37 , 38 , 39 , 40 ]. The instrumental influence in the photoacoustic experiment can be described by the transfer function H ( f ), which distorts the photoacoustic signal of the sample δp total ( f ), in the following way:

…”

Section: Methodsmentioning

confidence: 99%

“…The earlier developed procedure based on neural networks [ 10 , 11 , 29 , 30 , 31 ] for processing of experimentally recorded photoacoustic signals of silicon samples by the open photoacoustic cell [ 32 , 33 , 34 , 35 ] shows effective recognition and removal of instrumental influence [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ], and, consequently, provides a detailed and precise characterization of the sample [ 41 , 42 , 43 , 44 , 45 , 46 ]. On the other hand, a very thin TiO 2 layer (nano-layer) is easily deposited in a silicon substrate.…”

Section: Introductionmentioning

confidence: 99%

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Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

et al. 2023

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In this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO2 film deposited on a silicon substrate, with a thickness of 30 μm, in the frequency range of 20 to 20 kHz with neural networks were analysed. For this purpose, the geometric (thickness), thermal (thermal diffusivity, coefficient of linear expansion) and electronic parameters of substrates were known and constant in the two-layer model, while the following nano-layer thin-film parameters were changed: thickness, expansion and thermal diffusivity. Predictions of these three parameters of the thin-film were analysed separately with three neural networks. All of them together were joined by a fourth neural network. It was shown that the neural network, which analysed all three parameters at the same time, achieved the highest accuracy, so the use of networks that provide predictions for only one parameter is less reliable. The obtained results showed that the application of neural networks in determining the thermoelastic properties of a thin film on a supporting substrate enables the estimation of its characteristics with great accuracy.

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…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

et al. 2023

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“…The minimum volume cell configuration is a kind of transmission cell configuration in which the sample is put directly on the top of the microphone, as presented in Figure 1, [12], so the microphone chamber becomes the PA cell, closed by the sample on one side and the microphone diaphragm on the other one. This kind of PA cell becomes the reason for disorders of the recorded signal on its endings [13], so if we want to utilize the whole measured frequency range, it is needed to know the behavior of the microphone.…”

Section: Theoretical-mathematical Model Of Photoacoustic Responsementioning

confidence: 99%

Using simulated data in computationally intelligent photoacoustics

Jordović-Pavlović,

Raspopović-Milić,

Đorđević

et al. 2023

Engineering Today

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A development of simulated data and discussion on potential use in processing photoacoustic measurement data, as well as in solving of an inverse problem are presented in the paper. Simulated experimental values are obtained based on theoretical-mathematical model of photoacoustic response, described in the paper. Credibility of the data is acquired due to the experimental measurements. Simulated data validity was proven with the satisfactory accuracy, precision, and reliability of the several machine learning models which were trained on the designed database and are applied in processing photoacoustic measurement data. Presented results show that if a huge dataset of high-quality data is generated, solving issues of inverse photoacoustic problem is successful, computing is cost effective, and process rate of machine learning models is suitable for real time work.

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“…The relation among different elements of the analogous electro-acoustical system and the actual geometrical values of the microphone are given in the following set of expressions [30]:…”

Section: Helmholtz Resonances-the Influence Of the Measurement Chainmentioning

confidence: 99%

Developing the Techniques for Solving the Inverse Problem in Photoacoustics

Nesic

Popović

Galović

2019

Atoms

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In this work, theoretically/mathematically simulated models are derived for the photoacoustic (PA) frequency response of both volume and surface optically-absorbing samples in a minimum volume PA cell. In the derivation process, the thermal memory influence of both the sample and the air of the gas column are accounted for, as well as the influence of the measurement chain. Within the analysis of the TMS model, the influence of optical, thermal, and elastic properties of the sample was investigated. This analysis revealed that some of the processes, characterized by certain sample properties, exert their dominance only in limited modulation frequency ranges, which are shown to be dependent upon the choice of the sample material and its thickness. Based on the described analysis, two methods are developed for TMS model parameter determination, i.e., sample properties which dominantly influence the PA response in the measurement range: a self-consistent procedure for solving the exponential problems of mathematical physics, and a well-trained three-layer perceptron with back propagation, based upon theory of neural networks. The results of the application of both inverse problem solving methods are compared and discussed. The first method is shown to have the advantage in the number of properties which are determined, while the second one is advantageous in gaining high accuracy in the determination of thermal diffusivity, explicitly. Finally, the execution of inverse PA problem is implemented on experimental measurements performed on macromolecule samples, the results are discussed, and the most important conclusions are derived and presented.

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Helmholtz Resonances in Photoacoustic Experiment with Laser-Sintered Polyamide Including Thermal Memory of Samples

Cited by 17 publications

References 21 publications

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

Using simulated data in computationally intelligent photoacoustics

Developing the Techniques for Solving the Inverse Problem in Photoacoustics

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