Hand prostheses partially restore hand appearance and functionalities. In particular, 3D printers have provided great opportunities by simplifying the manufacturing process and reducing costs. The “Federica” hand is 3D-printed and equipped with a single servomotor, which synergically actuates its five fingers by inextensible tendons; no springs are used for hand opening. A differential mechanical system simultaneously distributes the motor force on each finger in predefined portions. The proportional control of hand closure/opening is achieved by monitoring muscle contraction by means of a thin force sensor, as an alternative to EMG. The electrical current of the servomotor is monitored to provide sensory feedback of the grip force, through a small vibration motor. A simple Arduino board was adopted as the processing unit. A closed-chain, differential mechanism guarantees efficient transfer of mechanical energy and a secure grasp of any object, regardless of its shape and deformability. The force sensor offers some advantages over the EMG: it does not require any electrical contact or signal processing to monitor muscle contraction intensity. The activation speed (about half a second) is high enough to allow the user to grab objects on the fly. The cost of the device is less then 100 USD. The “Federica” hand has proved to be a lightweight, low-cost and extremely efficient prosthesis. It is now available as an open-source project (CAD files and software can be downloaded from a public repository), thus allowing everyone to use the “Federica” hand and customize or improve it.

The actual grip force provided by a hand prosthesis is an important parameter to evaluate its efficiency. To this end, a split cylindrical handlebar embedding a single-axis load cell was designed, 3D printed and assembled. Various measurements were made to evaluate the performances of the “Federica” hand, a simple low-cost hand prosthesis. The handlebar was placed at different angular positions with respect to the hand palm, and the experimental data were processed to estimate the overall grip force. In addition, piezoresistive force sensors were applied on selected phalanxes of the prosthesis, in order to map the distribution of the grasping forces between them. The electrical current supplied to the single servomotor that actuates all the five fingers, was monitored to estimate the force exerted on the main actuator tendon, while tendon displacement was evaluated by a rotary potentiometer fixed to the servomotor shaft. The force transfer ratio of the whole system was about 12.85 %, and the mean dissipated energy for a complete cycle of closing-opening was 106.80 Nmm, resulting lower than that of many commercial prostheses. The mean grip force of the “Federica” hand was 8.80 N, that is enough to support the user in many actions of daily life, also considering the adaptive wrapping capability of the prosthesis. On average, the middle phalanges exerted the greatest grip force (2.65 N) on the handlebar, while the distal phalanges a force of 1.66 N.

Schematic representation ofin silico-assisted screening of an AFM1 binding peptide and the working principle of toxin harvesting by molecularly endowed hydrogel.

The image-based approach is widely used in Fault Detection (FD) algorithms of mechanical systems. The images are derived from the vibrational signals transformed from the time to time–frequency domain, and they are used to develop a Convolutional Neural Network (CNN) to automate the FD process. Nowadays, images are also obtained from the transformation of vibrational signals from the time domain to Symmetrized Dot Pattern (SDP) coordinates, achieving high CNN testing accuracy. This paper shows a comparison of image-CNN approaches for FD using images obtained from time–frequency transforms and those obtained from the SDP transform as input. The comparison was conducted using experimental data from two publicly available bearing datasets, examining both the accuracy of the CNNs and the computational time required for the vibrational signal transformations. The results show that the SDP-CNN approach achieves the same accuracy as spectrogram-CNN approaches but with a significantly reduced computational time. These results support the future real-time implementation of the SDP-CNN approach for FD in mechanical systems such as bearings. Conflict of Interest Statement The authors declare no conflicts of interest.

A leading challenge in the assembly process of aircraft skin panels is the precise control of part-to-part gaps to avoid excessive pre-tensions of the fastening element which, if exceeded, impair the durability and the response under dynamics loads of the whole skin assembly. The current practice is to measure the gap in specific points of the assembly with parts already at their final location, and then be-spoke shims are machined and inserted between the mating components to fill the gap. This process involves several manual measurement-fit-adjust quality loops, such as loading parts on the assembly frame, measuring gaps, off-loading parts, adding be-spoke shims and re-positioning parts ready for the fastening operation—as a matter of fact, the aircraft is re-assembled at least twice and therefore the current practice has been proved highly cost and time ineffective. Additionally, the gap measurement relies on manual gauges which are inaccurate and unable to follow the actual 3D profile of the gap. Taking advantage of emerging tools such as in-line measurement systems and large-scale physics-based simulations, this paper proposes a novel methodology to predict the part-to-part gap and therefore minimise the need for multiple quality loops. The methodology leverages a physics-driven digital twin model of the skin assembly process, which combines a physical domain (in-line measurements) and a digital domain (physics-based simulation). Central to the methodology is the variation model of the multi-stage assembly process via a physics-based simulation which allows to capture the inherent deformation of the panels and the propagation of variations between consecutive assembly stages. The results were demonstrated during the assembly process of a vertical stabiliser for commercial aircraft, and findings showed a significant time saving of 75% by reducing costly and time-consuming measurement-fit-adjust quality loops.