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X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.
Quenching and partitioning (Q&P) steels are characterized by an excellent combination of strength and ductility, opening up great potentials for advanced lightweight components. The Q&P treatment results in microstructures with a martensitic matrix being responsible for increased strength whereas interstitially enriched metastable retained austenite (RA) contributes to excellent ductility. Herein, a comprehensive experimental characterization of microstructure evolution and austenite stability is carried out on a 42CrSi steel being subjected to different Q&P treatments. The microstructure of both conditions is characterized by scanning electron microscopy as well as X‐ray diffraction (XRD) phase analysis. Besides macroscopic standard tensile tests, RA evolution under tensile loading is investigated by in situ XRD using synchrotron and laboratory methods. As a result of different quenching temperatures, the two conditions considered are characterized by different RA contents and morphologies, resulting in different strain hardening behaviors as well as strength and ductility values under tensile loading. In situ synchrotron measurements show differences in the transformation kinetics being rationalized by the different morphologies of the RA. Eventually, the evolution of the phase specific stresses can be explained by the well‐known Masing model.
We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond.