Engineering education

Technical Drawing (TD) plays a foundational role in the education of undergraduate Industrial and Mechanical Engineering students in Italy. Despite being formally unified under the same academic disciplinary sector (IIND-03/B previously ING-IND/15), TD courses display significant heterogeneity across universities in terms of content, pedagogical approaches, and assessment strategies. This paper presents a comprehensive national mapping of TD instruction based on the systematic analysis of publicly available course syllabi from all Italian universities offering relevant degree programs. The study explores variations in curricular structure, teaching methodologies, and evaluation practices, highlighting emerging trends such as the integration of CAD tools, autonomous learning modules, and differentiated assessment pathways. The findings underscore both convergences and divergences in instructional design, laying the groundwork for a shared reflection among educators.

Keywords: Assessment Methods , Course Design , Curriculum Development , Engineering Drawing , Engineering Education

This paper explores the design, physical prototyping, and mechanical characterization of a haptic feedback system to be implemented in the simulation of Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) surgery. This technique is performed in extreme emergency situations, whereby it is hard to organize training on the patient under risk-free and controlled conditions. This constitutes the strong motivation for the realization of a simulator. The haptic feedback system consists of two different devices, each developed from an original concept and equipped with specific functions: a “push” device to simulate the axial force exerted on the catheter and perceived through haptic feedback by the surgeon; and a “torque” device to tighten or release the catheter when necessary, transferring torque resistance to the surgeon. After an in-depth analysis of the state of the art, the design is carried out with the aid of CAD and CAE software. The main components are manufactured through rapid prototyping techniques. The prototypes are tested qualitatively and quantitatively, proving their correct functionality in exerting sufficient force feedback.

Keywords: Catheter , Haptic device , Medical simulation

Analysing the particle size distribution (PSD) of nanomaterials is essential for many scientific and industrial applications, as PSD offers important details about the distribution, average size, and range of particles within a sample. All these properties have a huge impact on the characteristics and behaviour of the materials. In this work we investigated image-based PSD recognition of nanoparticles from synthetic SEM images. For the task we considered an image classification pipeline based on five conventional (hand-crafted) texture descriptors and five pre-trained convolutional neural networks (CNN). Fusion strategies for combining the best performing feature sets were also investigated. We found that both the conventional and the CNN-based features achieved good performance (best accuracy respectively 87.6% and 88.2%), whereas the optimal performance (90.2%) was obtained by combining the two best performing CNN-based feature sets. Altogether the results are promising and demonstrate the feasibility of the method.

Keywords: Deep Learning , Image processing , Nanomaterials , Scanning electron microscopy , Texture

This work aims to highlight the state-of-the-art framework for implementing digital twins to support the product-process development. More in detail, the literature re-view is discussed on the base of a precise industrial context offered by a research collaboration active between the authors and ABB S.p.A., Santa Palomba (Pomezia), Italy. With this aim, applications may be distinguished by the level of complexity and related methodologies, tools and hindrances. Thanks to their prior strategic organization, data collection, processing and analytics are sufficiently mature to allow for the development and testing of various artificial intelligence and digital twin related approaches. This study identifies the key technical and methodological constraints for implementing digital twins in industrial environments, providing a foundation for future structured development. A detailed discussion of the major implementation issues is then presented, with a focus on four key areas within the specific industrial context: Process, Quality, Maintenance, and Flows. The analysis highlights both the bottlenecks and the positive aspects that can influence the effectiveness of the next future implementations.

Keywords: Data Analytics , Industry 4.0 , Integrated Product-Process Development , Machine Learning , Model Based System Engineering (MBSE)

Industry X.0 robotic manufacturing demands higher accuracy and flexibility, enabling continuously adaptive processes designed and optimized through simulations and Digital Twins. To achieve this level of flexibility and productivity in high value-added processes, where limited robot position accuracy becomes a critical constraint, advanced engineering methods and digital tools are required. These solutions must predictively compensate for inevitable robot positional accuracy errors, eliminating the need for manual pose refinement and enabling the generation of “first-time-right” robot code. This work aims to address these challenges by introducing an engineering tool capable of predictively correcting robot positioning inaccuracies across the workspace, enabling accurate point-to-point motion generation. It is intended for tasks with limited process interaction forces, where positioning errors are dominated by geometric, compliance, and joint-related effects. The tool leverages a multi-parameter Machine Learning (ML) error predictor trained on a reduced experimental dataset, minimizing data acquisition time and production downtime. Realized as a Python-based framework, it can be seamlessly integrated into commercial offline programming environments to automatically generate validated robot programs. The paper details the framework structure, focusing on the definition of the ML-based position error predictor, and its implementation on a robotic cell equipped with a high-payload KUKA robot and a FARO laser tracker. A preliminary experimental analysis identified payload, approach direction, and point location as the key operational parameters, accessible at the code level, that influence positioning accuracy. These insights guided feature selection and the design of reduced training datasets. In particular, a uniform spatial grid of only 64 points, corresponding to about one hour of measurement time, was sufficient to achieve near-optimal model accuracy. Several ML algorithms were compared, with the Tabular Prior-data Fitted Network achieving superior generalization on small datasets. Experimental validation on the KUKA robot showed up to a 98.4 % reduction in positioning error and consistent performance across all tested points, confirming the tool robustness and suitability for deployment across different industrial environments. All datasets, source code, and implementation scripts are openly released to enable reproducibility and facilitate industrial deployment.

Keywords: Engineering tool , Industrial robot , Machine learning , Offline compensation , Position accuracy , Robot simulation

Design thinking (DT) is increasingly emerging as an effective approach to promote innovation in educational contexts. This article, starting from an overview of the literature reviews published in the last decade, aims to outline the main opportunities and challenges associated with the application of DT in both teaching practice and teachers' professional development. The adoption of DT can contribute to improving learning experiences in all disciplines, particularly in STEM fields, by fostering more inclusive, reflective and student-centered teaching approaches. In teacher education, DT encourages a vision of the educator as a designer of the learning experience, capable of addressing educational complexities through empathy, creative experimentation and iterative problem solving. However, the integration of design thinking in school and training contexts is not without its criticalities: it requires adequate institutional support, effective scaffolding strategies and a profound change in teaching practice.

Keywords: Design thinking , Educational innovation , Student-centered learning , Teacher professional development

Global demographic shifts, particularly the growing elderly population and their need for chronic disease management are straining healthcare systems. Innovative models that emphasize telemedicine and digitalization offer promising solutions to improve care and reduce costs. To evaluate the feasibility and sustainability of such a model, an adaptive mixed-methods approach, including dynamic context monitoring and a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, was employed. A preliminary three-month regional study aimed to prevent acute events in older adults and enhance the quality of life using digital devices for self-monitoring vital signs was conducted. These data were transmitted in real-time to a platform monitored by general practitioners. Despite the short experimental period, early findings are promising, indicating the model's potential for scalability and adaptability to various settings, such as protected discharge or chronic care programs. However, addressing challenges like internet connectivity, healthcare professional engagement, sociocultural resistance, and the need for ongoing training is crucial. Effectively managing these factors while leveraging existing strengths and opportunities could lead to more efficient, responsive healthcare systems.

Keywords: Healthcare , Healthy and Active Ageing , Older adults , Sustainability , Telemedicine

Wrist impairments are common in post-stroke and elderly populations, often requiring rehabilitation to restore joint mobility and strength. However, clinic-based programs pose accessibility challenges, especially for patients with limited mobility. This paper presents W.R.I.S.T. (Wrist Rehabilitation Intelligent Support Tool), a soft robotic prototype designed to facilitate wrist flexion-extension exercises in home settings. The system features an inflatable silicone handle integrated into a 3D-printed structure, equipped with a flexion sensor for monitoring and a pneumatic circuit for real-time resistance modulation. A microcontroller manages pressure regulation and sensor data acquisition, enabling adaptive therapies and personalized training. Experimental validation confirmed a predominantly linear relationship (R2 > 0.85) between sensor output and flexion angle across pressure levels, ensuring reliable motion tracking. Additionally, a game-based interface was developed to enhance user engagement, transforming repetitive exercises into an interactive task with customizable difficulty. The integration of adjustable mechanical resistance and interactive feedback supports progressive rehabilitation while promoting adherence. W.R.I.S.T. offers an affordable, customizable, and valid solution for decentralized rehabilitation, particularly suitable for elderly users or those with limited access to clinical services.

Keywords: 3D printing , Pneumatic , Rehabilitation , Soft Material , Wrist

This paper presents a comprehensive overview of methodologies developed to support blind and visually impaired people (BVIP) in perceiving two-dimensional visual content through tactile representations. Over the past decade, the TIP group at the Department of Industrial Engineering of Florence has advanced both deterministic and AI-assisted techniques to generate tactile bas-reliefs from pictorial and technical images. The research spans various computerassisted methodologies for producing depth images or full 3D models of tactile supports. The research seeks to establish a codified language, its expression dictated by the ‘grammar' of semi-automatic procedures that offer limited scope for a manual user's free interpretation. Latest results achieved in the translation of abstract paintings are presented, including an effective pixel intensity-based approach for translating chromatic information into tactile depth. Applications include cultural heritage accessibility and inclusive technical education. Results demonstrate the growing viability of hybrid and AI-enhanced solutions to generate meaningful, perceivable tactile models, highlighting the importance of structured tactile languages and multimodal interfaces for improving accessibility.

Keywords: 3D modelling , bas-relief , Blind People , Depth image , tactile models

In semiconductor environment, the manufacturing process and the related quality control hold a fundamental position. This aspect has been enhanced with the introduction of new materials, such as Silicon Carbide (SiC), and the necessity to perform a reliable method to detect wafer defects. In this paper, the Electrical Wafer Sorting (EWS), a reliable technique for creating electrical wafer maps, is combined with a deep learning pipeline to improve the defects identification. With respect to the state of the art, the innovative aspect is in the combination of a supervised system with an unsupervised sub-system. Compared to benchmarking algorithms present in literature, this approach allows to correctly classify well-know defect patterns and to characterize new defect morphology, leading to the identification of new potential issues in the production line and, overall, to a production yield improvement.

Keywords: Deep Learning , Electrical Wafer Sorting , Silicon Carbide , Wafer Defect Maps

Car seat comfort represents a fundamental dimension of automotive ergonomics, yet its assessment and optimization remain predominantly reliant on subjective feedback and iterative physical prototyping, limiting scalability and responsiveness in the design process. This study presents a novel data-driven framework that synergistically integrates statistical analysis and deep learning architectures enhanced with attention mechanisms to predict perceived driver discomfort from pressure distribution patterns. A comprehensive dataset acquired from 192 participants was used to extract biomechanical descriptors, such as contact area and peak pressure, across 9 anatomical regions, which were subsequently correlated with subjective discomfort ratings collected on a 10-point Likert scale. The proposed hybrid deep neural network achieved a modelling accuracy of R2 = 0.82 for overall discomfort, with peak pressure emerging as the most influential factor (Spearman's r = 0.78). Moreover, Finite Element Analysis (FEA) was employed to validate geometry refinements suggested by the AI model, resulting in an 18% reduction in peak seat pressure without compromising structural integrity. The findings underscore the transformative potential of integrating artificial intelligence with CAD–CAE methodologies to enable rapid, user-centered, and simulation-driven ergonomic design early in the vehicle development cycle, minimizing the need for physical prototypes and enhancing occupant comfort.

Keywords: AI-Driven Comfort Models , Deep Learning with Attention Mechanisms , Ergonomic Seat Design Optimization , Finite Element Validation of Human-Centric Design , Pressure Mapping and Biomechanical Analysis

Background: Pumping in Laser-class sailing is a dynamic propulsion technique used in marginal wind conditions and characterized by repetitive, coordinated oscillations of the sailor–sail system. Despite its practical relevance, its biomechanical and ergonomic demands remain insufficiently characterized. Methods: A mixed-methods framework was applied combining questionnaire data, kinematic analysis, ergonomic assessment, and musculoskeletal modelling. Thirty-six competitive Laser sailors completed a Borg CR-10-based questionnaire on perceived discomfort/fatigue across body regions at predefined time points (during pumping, immediately after training, and the following day). A controlled land-based multi-angle video acquisition was used to reconstruct a standardized pumping posture and parameterize a digital human model in DELMIA® for postural/kinematic analysis. Ergonomic risk was assessed using REBA, and muscle activity was estimated using the AnyBody® Modeling System (simulation-derived normalized muscle activity across 129 muscles). Results: the simulation identified high neuromuscular demand in the trunk and shoulder complex, with several deep trunk stabilizers and the left latissimus dorsi reaching 100% modeled normalized muscle activity. Marked lateral asymmetry was observed, with right-sided trunk dominance and left-sided shoulder dominance. Kinematic analysis showed substantial joint excursions, with large lumbar motion amplitudes, while REBA yielded a score of 11 (Very-High Risk). Questionnaire data indicated a high prevalence of pumping-related musculoskeletal discomfort (72.2%), most frequently involving the lower back, shoulders, and knees. A dissociation was observed between modeled muscle activity and perceived fatigue, with the lower limbs rated as most fatigued despite lower modeled activation than the trunk. Conclusions: Findings identify the deep trunk stabilizers, latissimus dorsi, and lower extremities as key regions involved in pumping, with marked lateral asymmetry and high ergonomic risk. They support targeted training, injury-prevention, and ergonomic strategies to improve performance and reduce injury risk in competitive sailing.

Keywords: discomfort , laser sailing , muscle activation , musculoskeletal simulation , pumping maneuver , REBA ergonomic assessment

This investigation examined the perceptual-motor distortions induced by mixed reality head-mounted displays during spatial navigation tasks. Employing a two-phase experimental paradigm, we initially quantified kinematic differences between helmet and non-helmet conditions, subsequently assessing sensorimotor adaptation through repeated bidirectional trajectory navigation. Fifty participants executed standardized trajectories while three-dimensional movement kinematics were recorded via spatial tracking. Statistical analyses revealed significant alterations in movement parameters during helmet use, characterized by increased trajectory tortuosity and elevated oscillation frequency. Qualitative assessment of movement patterns demonstrated systematic lateral deviations perpendicular to the intended vector, suggesting distortion of depth perception and spatial mapping processes. Longitudinal analysis of adaptation metrics indicated a 42.72% reduction in oscillation frequency between early and late trials, though this effect did not reach conventional statistical significance. These findings contribute to the theoretical understanding of technologically induced perturbations to spatial cognition and motor control, while establishing trajectory tortuosity as a sensitive metric for quantifying mixed reality performance decrements. The documented adaptation phenomena have significant implications for training protocols in professional applications of mixed reality technology.

Keywords: Head-Mounted Display , Mixed Reality , Spatial Navigation , Tortuosity

Point clouds have become a widely used data format in computer vision, driven by the increasing availability of 3D scanning devices in applications such as robotics and autonomous driving. However, challenges in data acquisition, such as occlusion, reflection, and limited sensor resolution, often result in incomplete scans. Consequently, point cloud completion, the task of reconstructing the complete geometry of a point cloud object from partial data, has emerged as a key area of research. Numerous deep learning-based methods have been proposed to tackle this problem. Although some surveys on shape completion exist, the rapid growth of research in recent years calls for an updated review. In this work, we present a comprehensive survey of recent advancements in deep neural networks for point cloud completion, covering literature from 2024 up to December 2025. After establishing a reference context by analyzing the major foundational methods up to the year 2023, we review the most recent methods with an analytical perspective based on how they have improved each specific component of the network. Furthermore, we present a comparative analysis of the performance of the reviewed methods on the main benchmark datasets, and finally, we discuss open challenges, offering insights and suggestions for future research directions.

Keywords: 3D Object Reconstruction , Computer Vision , Deep Learning , Point Cloud Completion , Review , Shape Completion

Objective: Pediatric airway management presents unique challenges, particularly in patients with congenital conditions that increase the risk of difficult intubation. Simulation offers a safe environment for clinicians to practice, yet current pediatric mannequins often lack pathological realism. This study aims to develop and validate a high-fidelity, patient-specific pediatric airway mannequin simulating difficult intubation associated with Crouzon syndrome. Technology or Method: A modular training mannequin was developed through CT-based modeling and additive manufacturing at the T3Ddy Laboratory, a collaboration between AOU Meyer Children's Hospital-IRCCS and the University of Florence. Anatomical structures were segmented from CT and MRI scans of a 19-month-old patient with Crouzon syndrome. These 3-D models guided the design of a realistic airway, including nasal and oral access, a flexible tongue, and a movable jaw. Rigid parts were 3-D-printed; soft tissues were cast in silicones of varying hardness. Design choices were validated through iterative testing and refinement. Results: The simulator was evaluated during a pediatric bronchoscopy training course. Physicians assessed realism, tactile feedback, and usability via a Likert-scale questionnaire. Results indicated strong agreement on the mannequin's effectiveness in replicating challenging intubation scenarios, particularly those requiring fiberoptic-guided techniques. Conclusion: This modular, patient-specific pediatric airway simulator provides a realistic, high-fidelity platform for training clinicians in difficult intubation techniques. Its anatomically accurate design enhances procedural confidence and skill acquisition, particularly for managing complex cases like those associated with Crouzon syndrome.

Keywords: 3-D printing , additive manufacturing , Crouzon syndrome simulator , pathological airway , pediatric simulation

This study investigates how the immersion level, experienced by users during their interaction with a virtual museum application, affect Visitor Experience and Learning Performance. A between group experimental study, has been conducted in a laboratory setting. A total of 60 participants has been randomly divided in two groups (A and B). Group A used a virtual museum application to interact with two digital replicas of archaeological finds via a low immersive desktop-based interface. Group B interact with the same application using a high immersive HMD-based interface. Immediately after the interaction with the VR application, participants were asked to answer a questionnaire in order to measure their perceived visitor experience (VX), their attitude toward the experience and their learning performance in terms of immediate knowledge retention. The results suggest that the use of HMD-based interface can improve the quality of VX and the attitude toward the experience reported by the participants, but it can negatively affect the resulting learning outcome. These findings emphasize the need for museum curators to find a balance between utilizing technology for attract new visitors and preserving the educational value of the exhibits.

Keywords: Immersion , Learning Performance , Virtual Museum , Virtual Reality , Visitor Experience

This paper aims to evaluate whether augmented reality (AR) visualisations can improve engineering students' learning experience. Three AR applications were developed and then integrated into classroom exercises in a Computer-Aided Design (CAD) course. The students used applications on their smartphones, through which they manipulated AR parts' models, visualised components' main views and assemblies' compositions. Subsequently, students completed a dedicated questionnaire, composed of five sections. Results showed that students prefer innovative visualisation over the traditional one, improving their understanding of complex geometries, views and assemblies. Furthermore, the findings revealed that prior familiarity with AR and VR tools did not significantly influence perceptions. The applications' usability, assessed through the System Usability Scale (SUS), was acceptable, while the Short User Experience Questionnaire (UEQ-S) average scores were highly satisfactory.

Keywords: Augmented Reality , Engineering Education , Innovative Education

Road safety is still a critical concern for all countries of the world since most of the reports state that traffic fatalities remain very high. Despite the reduction of fatal situations while driving, traffic collisions, especially due to unsafe behaviors, are the leading cause of death among young individuals. Governments use safety campaigns to promote safer driving, but traditional methods such as videos conveyed through 2D screens may have a limited impact. Cinematic Virtual Reality (CVR), with its immersive features, could offer a more effective way to deliver road safety messages. This study proposes a between-subject design to explore how CVR influences attitudes, behaviors, and physiological responses related to traffic safety. A sample of 95 young participants, split into two groups, watched either a fear-based or a positive-based 360° CVR video. They completed the Attitudes Towards Traffic Safety (ATTS) and Driving Behavior Questionnaire (DBQ) at different stages (before and after CVR experience and in follow-up condition), as well as a User Experience Questionnaire (UEQ) and a Virtual Reality Sickness Questionnaire (VRSQ). ECG data were collected during the CVR experience to assess physiological response. The results demonstrate that CVR significantly impacts safety attitudes and behavior, with a statistically stronger effect observed in the fear-based approach. Furthermore, CVR markedly impacts users' physiological responses, as evidenced by ECG analysis. Physiological data underscore CVR's effectiveness in engaging users and reveal that responses vary significantly based on the nature of the immersive experience, highlighting CVR's potential as a targeted intervention tool for traffic safety education.

Keywords: Cinematic virtual reality , Educational intervention , Physiological response , Risky driving , Road safety , Young drivers

When analyzing the education process, and especially in technical disciplines such as engineering, the student orientation phase is one of the most important initiatives. The way in which students recognize, experience, and find meaning in the orientation activities is capable of orientating the subsequent development of student-oriented information and advice. This paper presents the research results from a three-year study at the University of Florence, followed along with more than 580 students enrolled in the Mechanical and Management Engineering undergraduate programs. To gather greater insight into students' previous experiences, decision-making processes, information searching patterns, and participation in institutional orientation experiences, a comprehensive questionnaire was designed. The results indicate how critical intrinsic motivation and individual interest in engineering, plus expectations about employment prospects, were in shaping students' academic decisions. There is evidence of changing trends in students' motivation when compared across the years, and the increasing significance of family in academic selection. In addition to identifying suggestions for improving online based information, incorporating alumni narrative experiences, and reinforcing relationships between schools and universities, this study demonstrates the capacity of perceptions analyses to enhance orientation practices. These insights may help organizations to develop stronger and more versatile orientation programs.

Keywords: Engineering education , Management engineering , Mechanical engineering , Orientation

Background: In recent years, the growing adoption of Virtual Reality (VR) and 3D printing technologies has revolutionized surgical training by providing innovative opportunities for hands-on education. This study investigates the combined use of VR and 3D printed personalized anatomical models and cutting guides within the field of oral and maxillofacial oncologic surgery. Materials and methods: A mandibular tumour case was developed using the proposed approach, integrating both virtual and physical tools. Feedback was gathered from twelve surgical residents regarding their understanding of the case, the effectiveness of the immersive and three-dimensional technologies, and their overall satisfaction with the training experience. Results: Participants reported enhanced comprehension of complex surgical scenarios and valued the practical utility of the VR simulator combined with 3D printed models. The immersive environment facilitated skill acquisition in a risk-free setting. Conclusion: The findings underscore the significant added value of integrating VR and 3D printing technologies in surgical training, preparation, and simulation. This approach offers a safe, effective training platform that improves readiness for complex procedures in oncologic surgery and has the potential to be extended to other branches of maxillofacial surgery.

Keywords: fibula free flap , mandibular reconstruction , maxillofacial surgery , surgical education , three-dimensional printing , user-centered design , virtual reality