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Ultrasonic spectroscopy has been used as a technique for measuring the size and
concentration of particles in suspensions and emulsions, usually in the pharmaceutical, food, petrochemical, and other industries. The ability to analyze highly concentrated heterogeneous systems and optically opaque samples, without the need of dilution, is the major advantage over traditional technologies, as light scattering. The principle of characterization consists of measurements of the acoustic properties in the samples and using ultrasonic waves propagation theories in heterogeneous systems to interpret the experimental data. Several studies about ultrasonic spectroscopy have been carried out considering monodisperse particles. However, the polydisperse particle distributions are often found in many practical applications, for example, in crude oil emulsions during droplet coalescence or flocculation, and have a significant influence on the viscosity of an emulsion. These features are usually important and need to be incorporated into acoustic theories. In this work, acoustic spectroscopy within the frequency range 4-14MHz was used to measure the droplet size distribution of water-in-sunflower oil emulsions for a volume concentration range of 10 to 50%, considering the monomodal and bimodal behaviors. For this, an ultrasonic measuring cell was developed from the work of Bjørndal and Froysa (2008) using a relative measurement principle based on the
work of Kushibiki et al. (1995) to estimate the acoustic properties of emulsions.
Experimental procedures for emulsions preparation were elaborated to obtain different droplet size distributions. The results obtained by the experimental attenuation spectra were compared with acoustic models starting from those simplest models usually applied for the long wavelength regime, such as Isakovich (1948), core-shell developed by Hemar et al. (1997), coupled phase developed by Evans and Attenborough (1997), the alternative model to ECAH theory for simple scattering proposed by McClements and Coupland (1996), multiple scattering model developed by Waterman and Truell (1961) and the multiple scattering model extended developed by McClements, Hemar and Herrmann (1999), followed by elastic scattering model developed by Faran (1951), applied to the intermediate wavelength regime. The droplet size distributions were obtained from the
theoretical and experimental attenuation spectra using a mathematical inversion
procedure based on nonlinear optimization techniques. The results obtained by ultrasonic spectroscopy showed in agreement with those obtained by laser diffraction. The findings indicate that the methodology used in this study is suitable for polydispersed particle size characterization for moderate concentrations up to 20% and bimodal distributions for a concentration of 40% vol. The experimental and computational system developed can be applied to analyze the droplet size distribution from 0.1 to 500 m. Possible reasons for the discrepancies between the theoretical and experimental results are discussed.
This chapter discusses the essential components of microwave systems, which are relevant for designing a microwave radar measurement setup. The ways in which transmission lines are used in planar and waveguides lead to requirement for other components in planar or waveguide-based design. With a discussion on some primary antennas, the chapter reviews microwave absorbers and their characteristics. The power dissipation equation is established regarding the absorbing material. A few important microwave sources are discussed. The need for a mode converter in a microwave system leads to the necessary modes obtained from source-generated modes. A network analyzer is also discussed, which has the ability to measure the reflection loss and transmission loss of microwave power.
This paper investigates the performance of several Machine Learning (ML) techniques for online defect detection in the Laser Powder Bed Fusion (L- PBF) process. The research aims to improve the consistency in product quality and process reliability. The applications of acoustic emission (AE) sensor to receive elastic waves during the printing process is a cost-effective way of materializing such a demand. In this study, the process parameters were intentionally adjusted to create three different levels of defects in H13 tool steel samples. The first class was printed with minimum defects, the second class had only intentional cracks, and the last class included both intentional cracks and porosities. The AE signals were acquired during the samples' manufacturing, and three different machine learning (ML) techniques were applied to analyze and interpret the dataset. First, a hierarchical K-means clustering is employed for labeling the data, followed by a supervised deep learning neural network (DL) to match acoustic signal with defect type. Second, a principal component analysis (PCA) was used to reduce the dimensionality of the dataset. A Gaussian Mixture Model (GMM) was then employed to enable fast defect detection suitable for online monitoring. Third, a variational auto-encoder (VAE) approach is used to obtain a general feature of the signal that could be an input for the classifier. A supervised DL trained on the H13 tool steel dataset successfully detected quality trends in AE signals collected from 316L samples. The VAE approach presents a novel method to detect defects in L-PBF processes that eliminate the need for model training in different materials.
