Virtual prototyping and digital twin

Lost-wax casting is an ancient technique of great precision, used for the creation of bronze artefacts. The artist makes the model in clay or plasticine and then creates the plaster or silicone mold. Inside the mould, wax is brushed to the ideal thickness of 5 to 6 mm and then wax “straws” were added at appropriate positions to allow optimal filling of the mould during bronze casting, starting with the lowest volumes. Finally, the wax model is incorporated into a refractory mass that surrounds it internally and externally and forms the cavity into which the bronze is poured. In this research, the results were obtained by analysing an innovative digital technique that avoids the creation of the mould and shrinkage during the intermediate stages of bronze production. In fact, using a 3D scanner and the latest rapid prototyping techniques, an accurate reproduction of the original clay model was recreated. The moulded model replaces the wax model. It is also enclosed between two forms of refractory material and, due to its similar melting temperature and flow properties, minimises build time and improves accuracy. The studied methodology also allows a more precise assignment and greater control of all surface thicknesses through a discretised digital finite element (FE) model analysis.

Keywords: Bronze sculptures' reconstruction , Laser scanning technology , Lost-wax bronze casting , Mold withdrawal , Rapid prototyping

Accurate three-dimensional (3D) reconstruction of individual dental elements from cone-beam computed tomography (CBCT) data is critical for adaptive prosthetic design and occlusal simulation in digital dentistry. However, challenges persist due to the complex morphology of teeth, the similar radiodensities of hard tissues, and the limited resolution of CBCT in capturing interproximal surfaces between adjacent teeth. This study presents a semi-automated, open-source compatible workflow that integrates CBCT image segmentation with the reconstruction of missing interproximal molar regions using Non-Uniform Rational B-Spline (NURBS) surface modeling. Molar datasets from CBCT acquisitions were segmented into 3D Slicer open-source software and refined to isolate individual teeth. Missing surface regions were reconstructed using third-degree NURBS patches in Rhinoceros® ver. 8 commercial software. Method reconstruction accuracy was quantitatively evaluated on 14 M by applying the method to manually created voids on intact tooth models. The average root-mean-square error (RMSE) and Hausdorff distance were 0.041 ± 0.015 mm and 0.049 ± 0.023 mm, respectively. The resulting models exhibited high surface continuity and anatomical fidelity, making them suitable for Computer Aided Engineering (CAE) analysis and prosthetic customization. This approach enables reproducible, high-precision dental modeling and represents a promising step toward fully automated 3D reconstruction workflows in clinical practice.

Keywords: CBCT segmentation , Digital prosthodontics , Molar morphology subdivision , NURBS modeling

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

This study investigates the development of advanced technologies for subsurface monitoring, with significant implications in geophysics, natural hazard mitigation, and environmental engineering. Specifically, the research focuses on the application of Distributed Acoustic Sensing using a 28 km long scientific electro-optical cable deployed in deep-sea conditions as a sensor for the detection of vibrations and mechanical disturbances along its path. DAS enables continuous, high-resolution monitoring of the underwater acoustic environment. However, the system's sensitivity and accuracy are strongly influenced by the degree of mechanical coupling between the optical fiber and the surrounding medium, which in turn depends on both the structural characteristics of the cable and the nature of the seabed where it is installed. To address these challenges, a digital twin of the experimental configuration has been developed. This virtual model allows for the simulation and optimization of the cable's mechanical behavior under various environmental conditions. Particular emphasis is placed on the analysis of the cable's static equilibrium state, which is essential for understanding the actual transmission of mechanical stress to the optical fiber and for the calibration of numerical models against experimental data. The manuscript is organized as follows: Introduction, Cable Architecture, Experimental Setup, Methodology, Results, and Conclusions.

Keywords: Digital twin , Distributed Acoustic Sensing (DAS) , Mechanical Coupling , Optical Fibers

The GeoINQUIRE project (GEO_INQUIRE, HORIZON-INFRA-2021-SERV-01, GA - 101058518) is a European initiative aimed at developing innovative research infrastructures for monitoring the subsurface, with applications in the fields of geophysics, natural hazards and environmental engineering. In this context, the Laboratori Nazionali del Sud – Istituto Nazionale di Fisica Nucleare are working on the application of Distributed Acoustic Sensing (DAS) technology, using a proprietary 28 km-long scientific deep-sea electro-optical cable as sensor to detect vibrations and mechanical changes along its path, to provide continuous, high-resolution monitoring of the soundscape. The effectiveness of DAS in such applications depends on the ability to accurately simulate the response of the system, since the mechanical coupling between optical fiber and soil, mediated by the cable jacket, varies significantly depending on the type of cable used and the characteristics of the soil (rocky, sandy, etc.). To address this issue, the development of a digital twin of the configuration has been initiated to model, simulate and optimize the behaviour of the optical fiber under different operating and environmental conditions and using cables different manufacture. The integration of experimental data into digital simulations enables the prediction and improvement of DAS performance, helping to optimize design solutions and promote the evolution of smart manufacturing.

Keywords: Digital twin of optical fiber cable , Distributed Acoustic Sensing , Mechanical coupling , Modal analysis , Optical fibers cable manufacture

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

The use of multi-layered foam pad as filler for cushions, seats components and furniture, is widely spread in many industries, especially in car-seat manufacturing ones. The mechanical behavior of these materials is directly connected to the perceived comfort and discomfort by users. This work presents a numerical investigation of the mechanical response of two foam materials under localized compression loading: a standard polyurethane foam and a viscoelastic memory foam. The analysis focuses on two key phenomena relevant to comfort applications: pressure absorption, i.e. the material's ability to attenuate peak stresses, and pressure diffusion, i.e. the spatial redistribution of contact forces across internal layers. Finite Element Method (FEM) simulations were performed on a layered foam block (200 × 200 × 100 mm), indented centrally by a rigid cylindrical indenter (30 mm diameter) up to 50 mm depth. The block was discretized into ten 10-mm-thick layers, enabling pressure analysis throughout the thickness. Results show that memory foam achieves significantly lower peak pressures and smoother vertical gradients compared to standard foam, indicating superior performance in both absorption and diffusion. Conventional foam, while more resilient, exhibits greater pressure concentration at deeper layers. These outcomes support the use of memory foam in applications requiring high comfort and soft tissue protection, such as bedding, seating, and wearable protective equipment. Moreover, the study highlights FEM is effective for predicting pressure behavior in layered, deformable media.

Keywords: Finite Element Method , Pressure distribution , Viscoelastic foams

The EcoDAM (Eco-Design for Additive Manufacturing) project presents a comprehensive framework aimed at enhancing the environmental sustainability of metal additive manufacturing (AM) processes. Recognizing the complex interdependencies between material selection, manufacturing technologies, and lightweight design, the framework integrates four key steps: sustainable technology selection, lightweight design optimization, and a two-phase parametric life cycle assessment (LCA) involving both objective function formulation and constraint definition. Innovative methodologies such as function-oriented patent analysis, topological and lattice structure optimization, and parametric LCA modelling are employed to systematically evaluate and minimize environmental impacts throughout the AM product lifecycle. A case study based on Ti6Al4V connecting rod produced by laser powder bed fusion demonstrates the application of the framework, resulting in more than 50% weight reduction and improved sustainability performance. Key parameters, such as cell size and wall thickness of gyroid lattice structures, are optimized using finite element modelling simulations, while LCA constraints are formulated using experimental and literature-based data. The framework's modular design allows for industrial integration using commercial tools, enabling scalable, data-driven, and environmentally informed decision-making in AM. EcoDAM thus contributes a robust methodology for advancing circular economy goals in industrial AM.

Keywords: Additive manufacturing , Eco-design , Lightweight design , Parametric LCA , Patent analysis , Prospective LCA

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

Ankle injuries are common due to the joint's role in weight-bearing and dynamic motion. Finite element method (FEM) has become a valuable tool for understanding joint biomechanics and planning clinical interventions. However, many existing models are limited by static simulations, simplified geometry, or generalised approaches that do not focus specifically on the talus-tibial joint. This study presents a subject-specific FEM framework of the ankle, integrating magnetic resonance imaging (MRI) and gait data from a healthy 49-year-old male. The tibia and talus were reconstructed from MRI and cartilages were modeled using a custom method to compensate for limitations in image resolution. Kinematic data was collected using the Xsens motion capture system and applied to a dynamic FEM developed in Abaqus. The model yielded contact pressures between 1.2 and 21.8 MPa and maximum von Mises stresses up to 5.7 MPa. The stress distribution was predominantly lateral, consistent with known biomechanical patterns. This framework allows realistic simulation of ankle mechanics and provides a basis for assessment of pathological conditions and surgical planning.

Keywords: 3D Modeling , Ankle , Finite Element Analysis , Gait Analysis

Tuning the operational parameters of complex handling machines involves a complex interplay of variables impacting the performance and reliability of the equipment and the processes being executed. By integrating advanced simulation tools in DTs architectures, manufacturers can predict and analyse the performance of machines under various settings and scenarios. This paper proposes a physics-based simulation framework designed for offline optimisation of machine parameters and for integration in Digital Twin applications to explore the configuration space of machine parameters for their selection and fine-tuning. The framework enables virtual exploration of the parameter space to identify optimal parameter settings in terms of productivity and stability for both design-phase analysis and machine setup optimisation. While developed as a simulation component suitable for integration within Digital Twin architectures, the current implementation operates independently of real-time data integration. A case study from the wood industry demonstrates the application and validation of the approach under realistic operational scenarios, showing the framework's potential for deployment in Digital Twin systems.

Keywords: Digital Twin , Simulation , Wood industry

This study investigates the dimensional change on sintering of products printed via the binder jetting (BJ) additive manufacturing (AM) process, using a powder mixture of stainless steel 316 L and 1 wt% red mud. This research is a part of a vast project aiming to develop continuous-feeding anodes for direct carbon fuel cells (DCFC). Cubes with a through-hole were printed to assess dimensional change during sintering. The geometries of the samples were carefully reconstructed before and after sintering using points acquired by a coordinate measuring machine (CMM). The results reveal a slightly lower shrinkage of the powder mix compared to the reference 316 L material. Novel scaling factors have been proposed to compensate for dimensional changes in future anode production. Additionally, the shape of the cubes was highly distorted after sintering, especially along the powder spreading direction. Finite element simulations reveal the impact of the density gradient in the green state on the origin of sintering deformation. This density gradient, estimated at approximately 0.5% of the relative density per millimeter, is likely caused by the poor flowability of the powder mix.

Keywords: Binder Jetting , Composite Material , Density Gradient , Dimensional Accuracy , Red Mud , Sintering Simulation

The aerospace industry increasingly relies on digital twins to estimate unmeasured quantities through Virtual Sensing techniques. In this context, this work presents the experimental validation of a computational fluid dynamics (CFD) model of an aluminum wing with a NACA 0018 profile. Wind tunnel measurements were collected at various flow conditions and compared against CFD simulations performed in Simcenter STAR-CCM+. The strong agreement, quantified through pressure distribution comparisons and Normalized Root Mean Square Error (NRMSE), confirms the reliability of the numerical model. Crucially, the validated CFD setup provides the basis for future implementation of Virtual Sensing schemes based on the Augmented Kalman Filter (AKF), enabling the estimation of aerodynamic pressure loads using limited sensor data. This validation step is thus essential to ensure the predictive quality of the digital twin in such Virtual Sensing frameworks for structural monitoring and control.

Keywords: Aerodynamics , CFD , Virtual Sensing , Wind tunnel

The growing need for sustainable mobility is driving the search for solutions to reduce energy waste and harmful environmental emissions. In this context, the design of vehicle lighting presents a promising approach. The use of composite materials, along with the automation of their manufacturing and assembly processes, is increasingly in demand to strike a balance between cost, lightweight, and the required performance of vehicle chassis. Carbon Fiber Sheet Molding Compounds allow complex car body parts to be produced through a one-step compression molding process. Manual labor represents a crucial operation in preparing the compound charge and subsequent manipulation towards compression molding. However, it directly affects productivity. The continuity between the preparation and manipulation phases can be relaxed by implementing robotic solutions to handle the pre-shaped uncured composite charge, automating loading onto the press mold. Efficient robotic handling requires grippers designed to match the shape of the compound charge. Specifically, the gripper must control the deformation of the temporary form of the composite charges, ensuring that the shape deviations stay within acceptable limits for proper fit in the compression mold. This work presents a systematic approach to designing and optimizing robotic grippers to pick up and place deformable compound layers. The key design parameter that guides this method is the maximum distortion threshold, which regulates the process in the two stages. The first stage focuses on finding the optimal balance between the lowest grasping points and acceptable distortion to define the grasping layout. In contrast, the second stage addresses the gripper structure, aiming to create the lightest, yet stiffest frame. The approach is fully integrated into a computer-assisted design platform.

Keywords: CF-SMC , Integrated design , Limp uncured composite geometries , PLM Platform , Robotic Grippers

Directed Energy Deposition (DED) is increasingly utilized for the construction of large components, repair of worn and damaged parts, and integration into Hybrid Manufacturing systems to leverage diverse technological features. A burgeoning application of DED is in Additive Remanufacturing (ARem), by which existing components can be enhanced with additional features and materials to improve functionalities. This paper presents a systematic and integrated approach for Design for Additive Remanufacturing (DARem), focusing on the optimization of product structural requirements to enhance performance and the process design to minimize flaws while ensuring material integrity and expected tolerances. The suggested approach involves product and process design phases within a computer aided design platform. Identification of critical issues in the original product design is the fundamental phase that drives the selection of the appropriate materials and quantities for deposition. Next, topological optimization is employed to shape and position additional volumes, generating enhanced design variants. A simulation phase ends the product design steps, assessing the actual improvement of the component over its original design. Subsequent phases are related to process design assessment. Selection of process parameters and build strategies, and next, the behavioral simulation of deposition are fundamental to verify feasibility and generate paths instructions. Finally, thermomechanical simulation is conducted to estimate the final component state accurately, ensuring it meets functional requirements before proceeding to actual manufacturing pre-processing, material deposition, and post-processing phases. A case study involving an automotive suspension component is used to demonstrate the feasibility of the proposed integrated approach. Experimental phases are performed to define DED material properties for the redesign and investigate the reliable simulation of process-induced distortions. By reducing stress in critical areas through localized deposition of 316L stainless steel alloy using laser powder DED, the study explores the impact of toolpath strategies on process-induced distortions through a Design of Experiments (DoE) approach. The results confirm the feasibility of the deposition process in meeting functional requirements, particularly geometrical tolerances, highlighting the potential of DARem for the redesign and enhancement of existing components.

Keywords: 316L, Remanufacturing , Directed Energy Deposition , Finite Element Method , Product-Process design

The integration of automation into the Compression Molding process for mass production of automotive components using Carbon Fiber Sheet Molding Compound (CF-SMC) represents a significant improvement in the sector. It improves efficiency, precision, and safety while reducing production costs and allowing product customization. A review of current technologies highlights the limitations of manual lay-up and the potential of automated pick-and-place systems to transfer uncured charges from the preform stage to the mold. Needle grippers have emerged as the most suitable solution to minimize the retention time of composite material in the heated mold, reducing the risk of premature curing. This study proposes a validation model to analyze the needle insertion process, which includes the piercing, detachment, transfer, and release phases, with the goal of developing a numerical model capable of accurately replicating real physical behavior. Focusing on the piercing phase, the analytical model identifies key design parameters that correlate the insertion force, the tilt angle, and the geometry of the needle and the CF–SMC. Subsequently, a hybrid FEM–SPH numerical model, known for its suitability to simulate large-deformation problems, was then implemented in Abaqus.

Keywords: Carbon Fiber Sheet Molding Compound (CF-SMC) , Compression molding , Integrated design , Robotic Grippers

This study aims to gather a sustainable understanding of additive manufacturing and other Manufacturing 4.0 approaches like horizontal and vertical integration and cloud computing techniques with a focus on industrial applications. The DfAM will apply 4.0 tools to gather product feasibility and execution, with CAE—FEM analysis and CAM. This publication focuses on the redesign of a vehicle suspension arm. The main objective is to apply innovative design techniques that optimize component performance while minimizing cost and time. The IDeS method and TRIZ methodology were used, resulting in a composite element, aiming to make the FDM-sourced process a viable option, with a weight reduction of more than 80%, with less material consumption and, hence, less vehicle energy consumption. The part obtained is holistically sustainable as it was obtained by reducing the overall labor used and material/scrap generated, and the IDES data sharing minimized rework and optimized the overall production time.

Keywords: additive manufacturing , IDeS , product design , product reverse engineering , TRIZ

The rapidly changing industrial landscape is causing companies to quickly adapt to new market demands. Major players in the mining and construction sectors are undergoing disruptive trends, including digitalisation and servitizations. Simulations and Digital Twins enable more flexible fleet management, advanced monitoring, prediction, and decision support. However, there is a need for making these support systems more accessible for all the stakeholders involved, from operators to site managers and higher ranks. A possibility is to leverage the explanatory capabilities of eXtended Reality and Artificial Intelligence. This preliminary research study examined three Decision Support System solutions that were characterised by different levels of visualisation, contextualisation, and immersion. A qualitative study of N = 10 industry experts provided insights that were coded into design guidelines. Practitioners identified an increasing demand for scalable, user-centric solutions that support stakeholders throughout the decision-making process. XR and AI were well-received, and the importance of integrating value-driven design principles to ensure system trustworthiness and usability was widely acknowledged. Highlights Development of three DecisionSupport Systems with varying immersion and functionalities (e.g. XR and AI) for quarry vehicle management. Analysis of criticalities and potential of existing solutions through focus groups with industry experts. Definition of a set of guidelines and insights to support Decision Support Systems designers and decision makers.

Keywords: artificial intelligence , Decision support systems , extended reality , focus group , guidelines

This paper introduces a Unity-based Virtual Reality (VR) system designed for real-time, incremental acquisition, processing, and interactive spatial filtering of LiDAR-generated point clouds on the Meta Quest 3 platform. Using an Intel RealSense L515 sensor and SLAM-inspired alignment techniques, the system enables dynamic, user-defined spatial segmentation through bounding boxes to refine collected data. The proposed framework supports multi-pass acquisitions, merging, and cleaning of point clouds, effectively addressing the limitations of traditional batch processing workflows. Additionally, the system provides an efficient modular structure that integrates live visualization using the Pcx Unity asset and optimizes performance through binary PLY file conversion. A case study on capturing a Meta Quest 3 protective case demonstrates the system's robustness and ability to preserve geometric fidelity across incremental scans, with minimal redundancy and automatic alignment. The results highlight the potential for future expansion into real-time volumetric reconstruction, automated point cloud registration, and advanced spatial computing applications. By combining modular software architecture with accessible hardware configurations, this work contributes a practical framework for immersive 3D data handling, aligning with ongoing research in real-time 3D reconstruction, interactive VR applications, and advanced point cloud processing.

Keywords: 3D Scanning , Point Cloud Processing , Virtual Prototyping , Virtual Reality

The production of Engineered Wood Products (EWPs) — such as Oriented Strand Boards (OSB), particleboards, and fiberboards — plays a key role in the modern wood value chain, especially in the context of circular economy and climate change mitigation. By incorporating wood residues and low-quality timber into long-lasting products, these technologies contribute to carbon storage for up to 35 years, as recognized by EU regulations on carbon removals. However, the drying phase of wood particles is among the most energy-intensive steps in EWP manufacturing. This paper presents the development of a physical simulator aimed at optimizing the operational parameters of industrial dryers. The devised prototype will allow users to simulate and test drying processes under varying conditions, helping manufacturers reduce energy consumption without compromising final product quality. The simulator is intended to support the transition towards more sustainable and efficient production practices in the wood industry. The work is the result of a collaboration between academia and industry and represents a step forward in applying digital technologies to traditional manufacturing processes.

Keywords: Life Cycle , Mechanical Design , Simulation , Wood Products