This paper presents a light and compact optical fiber Bragg Grating sensor for DC and AC magnetic field measurements. The fiber is coated by a thick layer of a magnetostrictive composite consisting of particles of Terfenol-D dispersed in a polymeric matrix. Among the different compositions for the coating that were tested, the best magnetostrictive response was obtained using an epoxy resin as binder and a 30% volume fraction of Terfenol-D particles with sizes ranging from 212 to 300 µm. The effect of a compressive preload in the sensor was also investigated. The achieved resolution was 0.4 mT without a preload or 0.3 mT with a compressive pre-stress of 8.6 MPa. The sensor was tested at magnetic fields of up to 750 mT under static conditions. Dynamic measurements were conducted with a magnetic unbalanced four-pole rotor

This paper analyses mechanical property prediction through Machine Learning for continuous fiber-reinforced polymer matrix composites printed using the novel Material Extrusion Additive Manufacturing technique. The composite is formed by a nylon-based matrix and continuous fiber (carbon, Kevlar, or fiberglass). From the literature, the elastic modulus and tensile strength were taken along with printing parameters like fiber content, fiber fill type, matrix lattice, matrix fill density, matrix deposition angle, and fiber deposition angle. Such data were fed to several supervised learning algorithms: Ridge Regression, Bayesian Ridge Regression, Lasso Regression, K-Nearest Neighbor Regression, CatBoost Regression, Decision Tree Regression, Random Forest Regression, and Support Vector Regression. The Machine Learning analysis confirmed that fiber content is the most influential parameter in elasticity (E) and strength ($\sigma$). The results show that the K-Nearest Neighbors and CatBoost provided the closest predictions for E and $\sigma$ compared to the other models, and the tree-based model presented the narrowest error distribution. The computational metrics point to a size versus prediction time tradeoff between these two best predictors, and adopting the prediction time as the most relevant criterion leads to the conclusion that the CatBoost model can be considered, when compared to the others tested, the most appropriate solution to work as a predictor in the task at hand.

The effect of hydrostatic pressure on the waveguiding properties of high birefringence photonic crystal fibers (HiBi PCF) is evaluated both numerically and experimentally. A fiber design presenting form birefringence induced by two enlarged holes in the innermost ring defining the fiber core is investigated. Numerical results show that modal sensitivity to the applied pressure depends on the diameters of the holes, and can be tailored by independently varying the sizes of the large or small holes. Numerical and experimental results are compared showing excellent agreement. A hydrostatic pressure sensor is proposed and demonstrated using an in-fiber modal interferometer where the two orthogonally polarized modes of a HiBi PCF generate fringes over the optical spectrum of a broad band source. From the analysis of experimental results, it is concluded that, in principle, an operating limit of 92 MPa in pressure could be achieved with 0.0003% of full scale resolution.

A passive surface acoustic wave (SAW) pressure sensor was developed for real-time pressure monitoring in downhole application. The passive pressure sensor consists of a SAW resonator, which is attached to a circular metal diaphragm used as a pressure transducer. While the membrane deflects as a function of pressure applied, the frequency response changes due to the variation of the SAW propagation parameters. The sensitivity and linearity of the SAW pressure sensor were measured to be 8.3 kHz/bar and 0.999, respectively. The experimental results were validated with a hybrid analytical–numerical analysis. The good results combined with the robust design and packaging for harsh environment demonstrated it to be a promising sensor for industrial applications.

This paper formulates and solves a time harmonic inverse scattering problem to reconstruct the effective stiffness distribution of an adhesive bond in a layered elastic plate. The motivation is based on the assumption that localized adhesion flaws that diminish bond stiffness also tend to diminish bond strength. The formulation is based on the invariant imbedding method, applies to isotropic and anisotropic elastic layers, and is essentially that of identifying embedded acoustic sources in elastic layered structures. This paper presents two solutions for the inverse problem: the Born approximation and the exact solution. The example calculations compare the two solutions and show that when imperfections are too large in either magnitude or extent the accuracy of the Born approximation breaks down. The impact of noise and uncertainties in the background properties in the inversion is also investigated. A regularization strategy is introduced in the exact solution that controls solution sensitivity in regions with low signal to noise ratio.