Politecnio die Bari - Catalogo di prodotti della Ricerca
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Performance Analysis of Physical Layer Security Secrecy Key Generation in Indoor Environment
No full textThe heterogeneous nature of the Sixth-Generation (6 G) communication systems, jointly with the rapid expansion of sensitive applications, bring several challenges to systems security. The Secrecy Key Generation (SKG) is a Physical Layer Security (PLS) technique in which two legitimate nodes negotiate a session key, taking advantage of the physical layer phenomena as a source of entropy. In this contribution, we investigate the impact of Uniform Linear Array (ULA) dipole spacing on the SKG performance considering an indoor application environment. We considered two legitimate nodes, i.e., Alice and Bob, that adopt the SKG protocol to negotiate their session key. A malicious node, i.e., Eve, tries to eavesdrop on the communication to obtain the key by performing the on-the-shoulder attack. Results demonstrate that smaller dipole spacings, i.e., dleq 0.5 λ result in lower channel and key correlations between Eve and the legitimate nodes, enhancing security. Larger spacings, however, create periodic zones of moderate correlation, potentially aiding Eve under certain angular positions
Buckling performance of elliptical and tori-spherical heads with multi-mode geometric imperfections and variable wall thickness
No full textThis study investigates the nonlinear buckling behavior of ellipsoidal and torispherical pressure vessel heads subjected to internal pressure, employing advanced finite element analysis to evaluate structural stability under realistic loading conditions. A key novelty of the work lies in the comprehensive, parametric investigation of both global and local geometric imperfections, including eigenmode-affine shapes, localized dimples, and circular cutouts. These imperfections are systematically varied in terms of type, amplitude, location, and distribution across multiple geometries. The simulations utilize geometrically nonlinear analysis with the arc-length (Riks) method, enabling accurate tracking of the post-buckling response and capturing sudden instability phenomena. A particular emphasis is placed on the sensitivity of buckling strength to imperfection characteristics. The results demonstrate that local dimples significantly reduce the buckling capacity, especially when positioned near the apex of the head. In contrast, imperfections located in less critical zones exert a more moderate influence. Additionally, the introduction of variable wall thickness, strategically increasing local thickness in high-stress regions, proves to be an effective design strategy for enhancing buckling resistance without excessive weight penalties. The study also reveals distinct imperfection sensitivity trends between ellipsoidal and torispherical heads, highlighting the importance of geometry-specific optimization. These findings provide valuable insights for the improved design, fabrication, and inspection of thin-walled pressure components in critical engineering applications
Optimising brightness and power enhancement trade-off in Cerium doped YAG 3D luminescent concentrator
No full textThe effects of individual abilities and training strategy on learning: an empirical investigation in assembly tasks
No full textIn current human-centric systems, assembly tasks have become increasingly complex due to the transition to mass customisation, necessitating a deeper understanding of the relationship between human abilities and learning phenomena. Unlike previous studies, this work introduces a novel approach by explicitly quantifying the impact of minimum repetition training strategy and individual abilities on learning. Laboratory experiments were carried out with 98 subjects. Planning and problem-solving skills of participants were assessed using the Tower of London test, while manual dexterity was measured via the Purdue Pegboard Test. Subjects were asked to assemble two Lego models of different complexity during three single-repetition training sessions spaced four to five weeks apart. Each session was conducted using either paper-based procedures or assistive technology as support. A mixed-effects analysis was carried out to model learning effect. Results show that regression models perform better when observed data on individual abilities are considered. Findings reveal a significant learning effect during training even if only a single repetition is performed once every month for each product complexity investigated. These insights would help companies, engineers and managers in designing effective training strategies tailored to groups of subjects with specific abilities, thus reducing time and economics efforts
The design of counselling and wellness spaces on university campuses
No full textUniversity students face complex psychological challenges that affect both their
academic performance and overall well-being. The PROBEN-MOEBIUS project aims
to promote psycho-physical health by designing adaptable counselling spaces and
AI-enhanced resting rooms on university campuses. Using an evidence-based and
interdisciplinary approach, this study explores the impact of spatial configurations,
nature integration, and immersive technologies—such as virtual reality and intelligent
mirrors—on mental health. A comparative analysis of international case studies
informs the development of scalable and replicable design interventions. Special focus
is given to the “mirror room”: an introspective, interactive installation using AI to
support emotional reflection. The research proposes a new model for student wellbeing
through the integration of environmental psychology, architecture, and digital
innovation
Dynamic vibration-based process monitoring of power-honed gears quality for automotive transmissions with an AI-integrated system
No full textAttualmente, i test al banco di fine linea sulle trasmissioni assemblate rappresentano una pratica standard adottata dai produttori per convalidare la qualità complessiva del sistema. Uno dei requisiti prestazionali chiave per le trasmissioni moderne è il comportamento vibroacustico. Oltre a problemi di progettazione o assemblaggio non ottimali, le principali fonti di rumore indesiderato nella trasmissione sono difetti di fabbricazione su ingranaggi e cuscinetti. Il rumore caratteristico degli ingranaggi che rotolano è definito come rumore lamentoso, particolarmente fastidioso per l'udito umano data la sua caratteristica tonale. Deviazioni periodiche sui fianchi dei denti e sul passo interrompono l'innesto coniugato degli ingranaggi, inducendo errori di trasmissione e aumentando l'eccitazione vibroacustica del sistema. Questi difetti, se non identificati tempestivamente, comportano lo scarto dell'intera trasmissione nei test di fine linea. Sebbene i test al banco consentano una caratterizzazione accurata del comportamento vibro-acustico della trasmissione in condizioni operative reali, le non conformità rilevate in questa fase comportano significative perdite economiche, derivanti da obiettivi di produttività ridotti, smontaggio delle trasmissioni e identificazione delle cause profonde, scarto o rilavorazione di componenti difettosi e, infine, azioni correttive, spesso con efficacia limitata a causa della scarsa integrazione tra i sistemi di fine linea e i processi produttivi a monte. Negli ultimi anni, il settore automobilistico ha prestato crescente attenzione alla riduzione delle emissioni acustiche dei sistemi di trasmissione. In primo luogo, le normative europee sul rumore (Regolamento (UE) n. 540/2014) impongono limiti sempre più stringenti ai livelli di rumorosità dei veicoli, incoraggiando i produttori a perfezionare sia la progettazione che il controllo delle sorgenti di rumore meccanico. In secondo luogo, l'adozione diffusa di veicoli ibridi ed elettrici ha cambiato il panorama acustico: l'assenza del motore a combustione interna, che un tempo mascherava parte del rumore della trasmissione, ora rende il rumore meccanico più percepibile. Inoltre, le maggiori velocità di rotazione e le tolleranze più strette richieste da questi sistemi aumentano la sensibilità anche a difetti minori. Infine, la percezione acustica di un veicolo è diventata un attributo distintivo dell'esperienza di guida e un indicatore chiave della qualità percepita. Di conseguenza, il controllo vibroacustico e la caratterizzazione della trasmissione sono diventati obiettivi strategici per garantire comfort, prestazioni e competitività del prodotto. I produttori di trasmissioni per autoveicoli adottano ampiamente processi di finitura dura sui denti degli ingranaggi utilizzando utensili abrasivi come la rettifica e la levigatura per correggere le distorsioni causate dai trattamenti termici, affinare il fianco del dente rispetto al profilo evolvente teorico e migliorare la finitura superficiale. Tra questi, il processo di levigatura meccanica offre una soluzione efficiente ed economica per soddisfare questi requisiti. Tuttavia, nella levigatura meccanica, deviazioni casuali nella qualità dell'ingranaggio pre-lavorato e instabilità dinamiche possono compromettere la qualità vibroacustica degli ingranaggi finiti, portando a scarti a fine linea. I metodi di Controllo Statistico di Processo sono ampiamente applicati per identificare deviazioni di qualità e come indicatori generali dei livelli di qualità attraverso misurazioni geometriche dei parametri degli ingranaggi secondo gli standard tecnici. Sebbene affidabili, questi approcci richiedono molto tempo, investimenti sostanziali e potrebbero non sempre rappresentare la qualità effettiva dell'intera popolazione, in particolare nel rilevamento di difetti associati a fenomeni transitori, come le instabilità dinamiche nelle macchine utensili. In questo contesto, la strategia più efficace per prevenire tali problemi è quella di rilevare condizioni di guasto della macchina e componenti difettosi direttamente durante il processo di lavorazione. Il monitoraggio delle grandezze di processo emerge come una soluzione efficace e robusta per identificare sia l'instabilità in-process delle macchine utensili sia le deviazioni sulla qualità del componente finale. In questo lavoro, è stato sviluppato un modello di monitoraggio di processo integrato con l'intelligenza artificiale basato sull'acquisizione di vibrazioni tramite accelerometri installati sui componenti della macchina, per intercettare i componenti in-process che non soddisfano i requisiti target. Il modello è stato addestrato su un set di dati raccolto durante un'ampia campagna sperimentale condotta in un contesto industriale. Diversi modelli di estrazione di caratteristiche sono stati adottati per confrontare le prestazioni; i risultati mostrano che il modello proposto è in grado di prevedere con elevata accuratezza la qualità finale dei componenti e di identificare le instabilità dinamiche di processo associate. È stato inoltre dimostrato che il modello può garantire un'accuratezza affidabile anche quando si utilizza un numero ridotto di campioni di addestramento, una caratteristica fondamentale per un'implementazione industriale efficace.Currently, end-of-line bench tests on assembled transmissions represent a standard practice adopted by manufacturers to validate the overall system quality. One of the key performance requirements for modern transmissions is the vibro-acoustic behaviour. Apart from suboptimal design or assembly-related issues, the main sources of unwanted transmission noise are manufacturing defects on gears and bearings. The characteristic noise of rolling gears is defined as whine noise, particularly annoying for human hearing given its tonal characteristic. Periodic deviations on tooth flanks and pitch disrupt the conjugate engagement of gears, inducing transmission errors and increasing vibroacoustic excitation of the system. These defects, if not identified promptly, result in the rejection of the entire transmission in End-of-Line tests. Although bench tests allow for an accurate characterization of the vibro-acoustic behaviour of the transmission under real operating conditions, non-conformity detected at this stage leads to significant economic losses, resulting from reduced productivity targets, transmissions disassembly and root causes identification, scrap or rework of defective components and finally corrective actions, often with limited effectiveness due to the weak integration between end-of-line systems and upstream manufacturing processes. On the last years, the automotive sector has paid growing attention to the reduction of acoustic emissions from transmission systems. Firstly, European noise regulations (Regulation (EU) No 540/2014) impose increasingly stringent limits on vehicle noise levels, encouraging manufacturers to refine both the design and the control of mechanical noise sources. Secondly, the widespread adoption of hybrid and electric vehicles has changed the acoustic landscape: the absence of the internal combustion engine, which once masked part of the transmission noise, now makes mechanical noise more perceptible. Furthermore, the higher rotational speeds and tighter tolerances required by these systems increase sensitivity to even minor defects. Finally, the acoustic perception of a vehicle has become a distinctive attribute of the driving experience and a key indicator of perceived quality. As a result, vibro-acoustic control and characterization of the transmission have become strategic objectives to ensure comfort, performance, and product competitiveness. Automotive transmission manufacturers widely adopt hard finishing processes on gear teeth using abrasive tools such as grinding and honing to correct distortions caused by heat treatments, refine tooth flank with respect to the theoretical involute profile, and improve surface finish. Among them, the power honing process provides an efficient and cost-effective solution to meet these requirements. However, in power honing, random deviations in the quality of the pre-machined gear and dynamic instabilities may compromise the vibro-acoustic quality of the finished gears, leading to end-of-line rejects. Statistical Process Control methods are widely applied to identify quality deviations and as overall indicators of quality levels through geometric measurements of gears parameters according to technical standards. While reliable, these approaches are time-consuming, require substantial investment, and may not always represent the effective quality of the entire population, particularly in detecting defects associated with transient phenomena, such as dynamic instabilities in machine tools. In this context, the most effective strategy to prevent such issues is to detect machine fault conditions and defective components directly during machining process. Monitoring of process quantities emerges as an effective and robust solution to identify in-process both machine tools instability and deviations on the final component quality. In this work, a process monitoring model integrated with artificial intelligence has been developed based on the acquisition of vibrations through accelerometers installed on machine components, to intercept in-process components that do not comply with the target requirements. The model was trained on a dataset collected during an extensive experimental campaign conducted in an industrial context. Different feature extraction models have been adopted to compare performance; results show that the proposed model is able to predict with high accuracy the final quality of the components and to identify the associated dynamic process instabilities. It was also shown that the model can guarantee reliable accuracy even when using a small number of training samples, a key feature for effective industrial implementation
Point Cloud Segmentation Using Model-Fitting, Artificial Intelligence and Local Curvature Techniques
No full textSemantic segmentation and point cloud classification within the context of Cultural and Architectural Heritage have become key topics of investigation in recent years, particularly due to advancements in Artificial Intelligence. While 3D data, acquired through methods such as laser scanning and photogrammetry, enable the generation of highly detailed representations of sculptures, buildings, and archaeological sites, they also present significant challenges in accurately distinguishing various architectural and structural components. This study proposes the development of an advanced methodological workflow, implemented in Python, which integrates well-established algorithms for model fitting (RANSAC), unsupervised learning as clustering (DBSCAN), and the analysis of geometric curvature within point clouds. The approach is fully automated and requires no manual pre-training, enabling the segmentation of elements such as pavement, benches, chairs, columns, vaults, and others through a clearly defined sequence of operations and precise parameter settings. The results obtained from this framework, applied to the
study of the interior layout of an apulian church, are transferable to other case studies, adaptable to the specific needs of the user, and provide a solid foundation for future developments in computational representation, with potential applications in CAD or HBIM environments
Herdonia 3.0. Strategies for knowledge and documentation of an archaeological area
No full textThe paper traces the various phases of research on the survey and representation of the archaeological area of Herdonia, which was known as a Roman municipium in the Republican age and lived its greatest splendour in the Augustan age. The use of increasingly up-to-date technologies and software, and the application of survey methodologies and representation techniques adapted to the different aims that the state of research gradually set, has resulted in the digitalization of a large part of the remains of the monuments that are currently visible, with a view to implementing what is now considered an indispensable stage of documentation for the knowledge and protection of the cultural heritage. The aim is to show the degree of complexity in the production of such documentation, both digital and up-to-date, in a site that has been very well studied in archaeological terms in past, but which suffers from the lack of recognition as an archaeological park
Laser-assisted Gas Incremental Forming of sheet metal
No full textThe present study proposes an innovative approach in sheet metal forming, named laser-assisted gas incremental forming, that combines a local laser heating with the action of a pressurized gas to incrementally deform metal blanks. Circular blanks were clamped at their borders using a specifically designed setup and subjected to the action of the pressurized gas on their lower surface, while being locally heated by a laser beam acting on the upper surface. Experiments were conducted with both a stationary and a moving laser spot, varying the laser power, the gas pressure and the laser scanning speed. Temperatures acquired by thermocouples allowed to both define the laser parameters and tune a 2D thermal model used to reproduce and investigate the heating phase of the process. The deformed profile as well as the thickness distribution were measured after the forming process. Metallographic analyses both before and after the forming process were conducted to analyse the microstructural evolution of the alloy during the forming process. Analyses revealed that the final shape of the samples deformed with a stationary spot was symmetric and well-fitted by a Gaussian profile, while when moving the laser spot along a linear track, the final shape and the thickness distribution got asymmetrical, the maximum thinning remained located close to the end of the linear track and, additionally, it decreased as the laser scanning speed increased (due to the shorter interaction time between the material and the laser radiation). Results demonstrate that, thanks to the very local heating, the deformation can be tailored not only spatially but also quantitatively by changing both the laser trajectory and the process parameters