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This article presents a method to use the dispersive behavior of ultrasonic guided waves and neural networks to determine the isotropic elastic constants of plate-like structures through dispersion images. Therefore, two different architectures are compared: one using convolutions and transfer learning based on the EfficientNetB7 and a Vision Transformer-like approach. To accomplish this, simulated and measured dispersion images are generated, where the first is applied to design, train, and validate and the second to test the neural networks. During the training of the neural networks, distinct data augmentation layers are employed to introduce artifacts appearing in measurement data into the simulated data. The neural networks can extrapolate from simulated to measured data using these layers. The trained neural networks are assessed using dispersion images from seven known material samples. Multiple variations of the measured dispersion images are tested to guarantee the prediction stability. The study demonstrates that neural networks can learn to predict the isotropic elastic constants from measured dispersion images using only simulated dispersion images for training and validation without needing an initial guess or manual feature extraction, independent of the measurement setup. Furthermore, the suitability of the different architectures for generating information from dispersion images in general is discussed.
Only the nano-scaled structure of the nanocomposite and the dispersion of nanoparticles within the polymer matrix harbor multifunctional potential including superior fire retardancy. Thus, this chapter focuses on the dispersion of nanoplates, based mainly on studies of layered silicates and graphene/graphene-related nanoplates. The nanostructure and properties of the nanocomposites are dependent mainly on thermodynamic and kinetic factors during preparation. Improving nano-dispersion often directly improves flame retardancy. Therefore, the modification of the nanoplates as well as the preparation of nanocomposites becomes very important to control this dispersion. The dispersion of nanoplates functions as a prerequisite for the formation of an efficient protective layer, changing the melt flow and dripping behavior, or the improvement of the char properties.
Multilayer graphene/chlorine-isobutene-isoprene rubber nanocomposites: the effect of dispersion
(2016)
Multilayer graphene (MLG) is composed of approximately 10 sheets of graphene. It is a promising nanofiller just starting to become commercially available. The Dispersion of the nanofiller is essential to exploit the properties of the nanocomposites and is dependent on the preparation method. In this study, direct incorporation of 3 parts per hundred of rubber (phr) MLG into chlorine-isobutene- isoprene rubber (CIIR) on a two-roll mill did not result in substantial enhancement of the material properties. In contrast, by pre-mixing the MLG (3 phr) with CIIR using an ultrasonically assisted solution mixing procedure followed by two-roll milling, the properties (rheological, curing, and mechanical) were improved substantially compared with the MLG/CIIR nanocomposites mixed only on the mill. The Young’s moduli of the nanocomposites mixed in solution increased by 38%. The CIIR/MLG nanocomposites produced via solution showed superior durability against weathering exposure.
Zusätze von wässrigen Kunststoffdispersionen verändern Festbetoneigenschaft, haben aber auch Einfluss auf die Verarbeitbarkeit des Frischbetons. Die Ergebnisse der Untersuchungen geben Anhaltspunkte dafür, wie solche Betone bei extremen, aber in der Praxis zu erwartenden klimatischen Beanspruchungen reagieren.