New durable elastomeric materials are commercially available for 3D printing, enabling a new class of consumer wearable applications. The mechanical response of soft 3D printed lattices can now be tailored for improved safety and comfort by (a) leveraging functional grading and (b) customizing the outer envelope to conform specifically to the anatomy of the subject (e.g. patient, athlete, or consumer). Furthermore, electronics can be unobtrusively integrated into these 3D printed structures to provide feedback relating to athletic performance or physical activity. A proposed sensor system was developed that weaves unjacketed wires at two distinct layers in a lattice to form a complex capacitor; the capacitance increases as the lattice is compressed and can detect lattice deformation. A structure was fabricated and demonstrated with both static compression as well as low-velocity impact to highlight the utility for wearable applications. This work is focused on improving the performance of American football helmets as highlighted by the National Football League (NFL) Helmet Challenge Symposium; however, the lattice sensing concept can be extended to metal and ceramic lattices as well - relevant to the automotive and aerospace industries.

Photoacoustic spectroscopy employs acoustic resonators for signal amplification. Resonators are usually closed, however, in some applications, open resonators are preferred. The opening deteriorates the photoacoustic signal hence reducing the sensitivity of the photoacoustic measurement. We present two new approaches for simulating the photoacoustic signal in open resonators using finite element modelling. The approaches are based on the amplitude mode expansion model and the viscothermal model with the opening modelled using perfectly matched layers and the boundary element method respectively. Additionally, the performance of the viscothermal model using perfectly matched layers for simulating open resonators is extended to the ultrasound region. The simulation results are verified by comparing them to photoacoustic measurements. The approaches provide an accurate basis for designing and optimizing open resonators with high sensitivity.

T-cell resonators have been used lately for non-invasive blood glucose measurements for photoacoustic spectroscopy on skin samples. A resonator has a significant role in determining the strength of the measured signal and the overall sensitivity of the sensor. Here we present results of the measurement of the photoacoustic signal of such a T-cell resonator. The signal is also modelled using the amplitude mode expansion method, which is based on eigenmode expansion and the introduction of losses in the form of loss factors. The measurement reproduced almost all the calculated resonances from the numerical models with fairly good agreement. The cause of the differences between the measured and the simulated resonances are explained. In addition, the amplitude mode expansion simulation model is established as a faster and computationally less demanding photoacoustic simulation alternative to the viscothermal model. The resonance frequencies from the two models differ by less than 1.8%. It is noted that the relative height of the amplitudes from the two models depends on the location of the antinodes within the different parts of the resonator. The amplitude mode expansion model provides a quick simulation tool for the optimization and design of macro resonators.

Nowadays, intelligent models can correctly detect faults by analysing signals from rotating machinery. However, most of the studies are run in controlled environments and the performance in industrial real-world environments is not yet fully validated. Hence, a suitable tool to implement fault diagnosers is transfer learning, this topic is under development and challenges persist. This paper proposes a framework for creating accurate 1D-CNN based fault classifiers that can be transferred between different rotating machines and working conditions. Multiple Bayesian processes select architecture parameters and hyperparameters, which minimize a loss function related to their transferability to other machines and to the same machine under different operating conditions (such as load and engine speed). The resulting model is fitted to heterogeneous fault diagnosis data resulting in a 1D-CNN ensemble that improves the performance of the unitary model. Subsequently, the transfer learning capability of the ensemble is analyzed on two source data sets using function and parameter based transfer. The results are compared with classical fault diagnosis classifiers. Finally, additional transfer operations on five target domain datasets validate our framework on limited labeled samples and allow interpretation of the ensemble results. The ultimate goal is to find an ensemble that can generalize fault diagnosis on rotating machinery for easy implementation and update in industrial settings.