Nowadays, a challenging scenario involving additive manufacturing (AM), or 3D printing, relates to concerns on the manufacturing of electronic devices. In particular, the possibility of using fused filament fabrication (FFF) technology, which is well known for being very widespread and inexpensive, to fabricate structures with embedded sensing elements, is really appealing. Several researchers in this field have highlighted the high electrical resistance values and variability in 3D-printed strain sensors made via FFF. It is important to find a way to minimize the electrical resistance and variability among strain sensors printed under the same conditions for several reasons, such as reducing the measurement noise and better balancing four 3D-printed strain gauges connected to form a Wheatstone bridge to obtain better measurements. In this study, a design of experiment (DoE) on 3D-printed strain gauges, studying the relevance of printing and design parameters, was performed. Three different commercial conductive materials were analyzed, including a total of 105 printed samples. The output of this study is a combination of parameters which allow both the electrical resistance and variability to be minimized; in particular, it was discovered that the “welding effect” due to the layer height and printing orientation is responsible for high values of resistance and variability. After the optimization of printing and design parameters, further experiments were performed to characterize the sensitivity of each specimen to mechanical and thermal stresses, highlighting an interesting aspect. A sensible variation of the electrical resistance at room temperature was observed, even if no stress was applied to the specimen, suggesting the potential of exploiting these materials for the 3D printing of highly sensitive temperature sensors.

Intravenous (IV) infusion is one of the most common therapies in hospitalized patients. Monitoring the flow rate of the fluid that is being administered to the patient is therefore very important for his safety, considering that both over-infusion and under-infusion can cause serious health problems. In this document, a novel method for monitoring the flow rate in IV infusions is presented, that is based on deep learning computer vision techniques. Basically, the drip chamber is filmed with a camera and object detection is used to count drops. The proposed method is therefore less invasive than other ones developed for this purpose. Experimental results show that it can produce an accurate real-time estimate of the instantaneous flow rate of the drip. For these reasons, the proposed method can be effectively adopted to implement monitoring and control systems for health facilities.

Additive manufacturing technologies allow the fabrication of smart objects, which are made up of a dielectric part and an embedded sensor able to give real-time feedback to the final user. This research presents the characterization of a low-cost 3D-printed strain sensor, fabricated using material extrusion (MeX) technology by using a conductive material composed of a polylactic acid (PLA)-based matrix doped with carbon black and carbon nanotubes (CNT), thus making the plastic conductive. A suitable measurement set-up was developed to perform automatic characterization tests using a high repeatability industrial robot to define either displacement or force profiles. The correlation between the applied stimulus and the variation of the electrical resistance of the 3D-printed sensor was evaluated, and an approach was developed to compensate for the effect of temperature. Results show that temperature and hysteresis affect repeatability; nevertheless, the sensor accurately detects impulse forces ranging from 10 g to 50 g. The sensor showed high linearity and exhibited a sensitivity of $0.077{\mathsf{\Omega }\mathrm{g}}^{-1}$ and $12.54 {\mathsf{\Omega }\mathrm{mm}}^{-1}$ in the force and displacement range of 114 g and 0.7 mm, respectively, making them promising due to their low cost, ease of fabrication, and possible integration into more complex devices in a single-step fabrication cycle.

In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity points, each of them characterized by a class describing the type of discontinuity, a position, and a value quantifying the entity of the impedance discontinuity. The neural network was trained using a great number of simulated signals, obtained with a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training process, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performances were assessed. In experimental tests, where the discontinuity points were capacitive faults, the proposed method was able to correctly identify 100% of the discontinuity points, and to estimate their position and entity with a root-mean-squared error of 13 cm and 14 pF, respectively.

In this paper we present a study of the repeatability of an innovative electromagnetic tracking system (EMTS) for surgical navigation, developed to overcome the state of the art of current commercial systems, allowing for the placement of the magnetic field generator far from the operating table. Previous studies led to the development of a preliminary EMTS prototype. Several hardware improvements are described, which result in noise reduction in both signal generation and the measurement process, as shown by experimental tests. The analysis of experimental results has highlighted the presence of drift in voltage components, whose effect has been quantified and related to the variation of the sensor position. Repeatability in the sensor position measurement is evaluated by means of the propagation of the voltage repeatability error, and the results are compared with the performance of the Aurora system (which represents the state of the art for EMTS for surgical navigation), showing a repeatability error about ten times lower. Finally, the proposed improvements aim to overcome the limited operating distance between the field generator and electromagnetic (EM) sensors provided by commercial EM tracking systems for surgical applications and seem to provide a not negligible technological advantage.

In this paper a new low-cost stretchable coplanar capacitive sensor for liquid level sensing is presented. It has been 3D-printed by employing commercial thermoplastic polyurethane (TPU) and conductive materials and using a fused filament fabrication (FFF) process for monolithic fabrication. The sensor presents high linearity and good repeatability when measuring sunflower oil level. Experiments were performed to analyse the behaviour of the developed sensor when applying bending stimuli, in order to verify its flexibility, and a thermal characterization was performed in the temperature range from 10 °C to 40 °C to evaluate its effect on sunflower oil level measurement. The experimental results showed negligible sensitivity of the sensor to bending stimuli, whereas the thermal characterization produced a model describing the relationship between capacitance, temperature, and oil level, allowing temperature compensation in oil level measurement. The different temperature cycles allowed to quantify the main sources of uncertainty, and their effect on level measurement was evaluated.