Giovanna Sansoni

This work presents a simple method to detect the fault of components in an injection group of a diecasting machine. A usual problem in predictive diagnostics in industrial applications is the lack of replicable failure and fatal data: the parameters of the diecasting process are often changed to adapt the machine to a new production cycle and this makes difficult to identify faults using automated data analysis. The proposed method is based on an algorithm able to re-train itself when a change in production is detected. The final prediction of each fault condition is performed by combining classical machine learning analysis and experts' knowledge of the field by identifying discriminant weights to insert in the machine learning analysis. These weights are quantities that represent the experts' knowledge and the algorithm take into account.

Additive manufacturing (AM) is gaining relevance for the freedom it gives to designers in experimenting topologically optimized components, especially those having lattice morphology. Indeed, these are of great interest in various application fields (automotive, biomedical, etc.) because, in addition to a significant mass reduction, lattice topology (micro-scale) can be tuned to provide the final product (macro-scale) with the specific properties it needs to exhibit. However, additive manufactured lattice structures are still to be fully investigated, given the mismatch between the designed and the manufactured final product. This paper presents some preliminary results from a multi-instrument approach, grounding on non-contact measurement techniques, to characterize lattice and trabecular structures in terms of dimensional accuracy, surface morphology, stress-strain distribution and modal behavior.

This paper is a first step towards a smart hand gesture recognition set up for Collaborative Robots using a Faster R-CNN Object Detector to find the accurate position of the hands in RGB images. In this work, a gesture is defined as a combination of two hands, where one is an anchor and the other codes the command for the robot. Other spatial requirements are used to improve the performances of the model and filter out the incorrect predictions made by the detector. As a first step, we used only four gestures.

The PMD Camboard Picoflexx Time-of-Flight (ToF) camera is evaluated against the Microsoft Kinect V2 to assess its performance in the context of markerless motion capture system for human body kinematics measurements. Various error sources such as the warm-up time, the depth distortion, the amplitude related error, the signal-to-noise ratio, and limitations such as their dependence on the illumination pattern and on the target distance, are studied and compared. The Picoflexx device is also compared to the Kinect V2 in relation to the quality of shape reconstructions, to assess its adequateness in modeling human body segments, and human body kinematics measurements. The final result of this paper is definitely useful to the research community, demonstrating that, even if the Picoflexx performs lower than the Kinect concerning the measurement performances, its behavior in estimating the volume of the body segments, the angles at the joints for human body kinematics, and the angle at the ankle in assisted walking applications is definitely satisfactory. These results are extremely significant to obtain accurate estimates of the parameters of the human body models in markerless motion capture applications, especially in laboratory-free environments, where compactness, lightweight, wireless connection, and low-power consumption are of outmost importance.

A machine learning technique was used to monitor the state of damage of steel specimens subjected to cyclic contact fatigue tests and to make a continuous measurement without coming into contact with the specimens examined. Six different sets of specimens were considered and the torque and vibration data obtained from their respective tests were analyzed. Different statistical and spectral characteristics were obtained from each dataset, to represent their information content. The correlation existing between the characteristics was studied to select only the essential ones. The global data volume was reduced by a principal component analysis. The data thus obtained were subsequently processed, and classified, developing a K-means algorithm. The results were formulated in probabilistic terms with the aid of membership charts, confusion matrices and probability curves. The results obtained from this first analysis are highly encouraging. The algorithm is able to assess the damage status of the specimens, as well as detect the operating conditions of the machine. A substantial advantage of this method is the ability to estimate quantitatively the damage by calculating the distance of the current data from specific references. The performances of this approach were numerically estimated in terms of sensitivity, specificity and accuracy.

The Bronze Age crossbar wheel found in the XIX century in Mercurago (Italy) is an amazing example of the technical innovations stimulated by the diffusion of horse draught war chariots in Europe across the third and second millennium b.C. In this paper the tools of modern engineering were used to study the structural issues concerning the wheel and the chariot it should be attached to. In particular, the laser-scanner technology and finite element analysis were used for investigating the dimensional, shape and assembly issues of the wheel, as well as for assessing its structural integrity under operating conditions. The role of the inserted nave, which could be similar to modern bushings, was particularly emphasised, as it is a very innovative solution for that time. The performance of a war chariot equipped by this wheel was studied by means of a vehicle dynamics simulation software, hypothesizing two different chariot structures on the basis of Armenian and Egyptian evidences respectively. The former is probably more similar to the chariot to whom the Mercurago wheel was attached; the latter is technically much more advanced. The results of the analysis allowed obtaining important information about the chariot stability, reliability and structural integrity.

The evaluation of the morphological characteristics of small resistance arteries in humans is challenging. The gold standard method is generally considered to be the measurement by wire or pressure micromyography of the media-to-lumen ratio of subcutaneous small vessels obtained by local biopsies. However, noninvasive techniques for the evaluation of retinal arterioles were recently proposed; in particular, two approaches, scanning laser Doppler flowmetry (SLDF) and adaptive optics, seem to provide useful information; both of them provide an estimation of the wall-to-lumen ratio (WLR) of retinal arterioles. Moreover, a noninvasive measurement of basal and total capillary density may be obtained by videomicroscopy/capillaroscopy. No direct comparison of these three noninvasive techniques in the same population was previously performed; in particular, adaptive optics was never validated against micromyography. In the current study, we enrolled 41 controls and patients: 12 normotensive lean controls, 12 essential hypertensive lean patients, nine normotensive obese patients and eight hypertensive obese patients undergoing elective surgery. All patients underwent a biopsy of subcutaneous fat during surgery. Subcutaneous small resistance artery structure was assessed by wire micromyography and the media-to-lumen ratio was calculated. WLR of retinal arterioles was obtained by SLDF and adaptive optics. Functional (basal) and structural (total) microvascular density was evaluated by capillaroscopy before and after venous congestion. Our data suggest that adaptive optics has a substantial advantage over SLDF in terms of evaluation of microvascular morphology, as WLR measured with adaptive optics is more closely correlated with the M/L of subcutaneous small arteries (r = 0.84, P < 0.001 vs. r = 0.52, P < 0.05, slopes of the relations: P < 0.01 adaptive optics vs. SLDF). In addition, the reproducibility of the evaluation of the WLR with adaptive optics is far better, as compared with SLDF, as intraobserver and interobserver variation coefficients are clearly smaller. This may be important in terms of clinical evaluation of microvascular morphology in a clinical setting, as micromyography has substantial limitations in its clinical application due to the local invasiveness of the procedure.