Virtual prototyping and digital twin

Food spoilage is a pervasive issue with significant implications for public health, economic stability, and environmental sustainability. Addressing food spoilage is essential to promoting healthier living, reducing economic waste, and ensuring sustainable practices. When storing food in refrigerators, it would be crucial to monitor at least factors such as temperature, humidity, and signs of spoilage to ensure food quality and safety. The paper presents a concept for a smart fridge equipped with sensors to monitor critical factors such as temperature, humidity, and spoilage indicators. These sensors collect real-time data, which can be visualized through a multisensory eXtended Reality (XR) Digital Twin that provides a highly realistic 3D visualization of the real fridge, incorporating dynamic temperature simulations represented through 3D color maps and an olfactory simulation that replicates the smell of rotten food. The first impressions of the Multisensory XR Digital Twin emphasize its effectiveness in bridging the gap between raw data and user perception, allowing for an immersive interaction with the fridge as if physically present. Key benefits identified include enhanced spatial awareness and realism, multisensory feedback, and greater user engagement.

Keywords: Circular Economy , Digital Twins , eXtended Reality , Multisensory experience

Structural topology optimization is a key method for designing lightweight and efficient components, particularly in additive manufacturing. In aerospace and automotive engineering applications, where components bear dynamic loads, it is crucial to account for natural frequencies and weight reduction. Traditional optimization methods often require labour-intensive manual setup, especially for tuning input parameters that influence the algorithm's performance and convergence. Machine learning offers an alternative, streamlining this process and reducing reliance on trial-and-error adjustments. This study introduces a machine learning-based framework to optimize input parameters—such as evolutionary rates and mesh dimensions—while ensuring the creation of lightweight, optimized structures with consistent natural frequencies. The framework uses a neural network trained on a dataset built to describe several configurations of a cantilever beam case study. The primary focus is determining the optimal dataset size for effective neural network training. The analysis aims to minimize dataset size, reducing the time and effort required for data preparation while avoiding overfitting in the neural network. A dissimilarity metric based on problem metadata is used to guide parameter tuning. After training, the artificial neural network is incorporated into the Bayesian Optimization framework as a surrogate model, enabling the new model to rapidly estimate outcomes without running full simulations. This approach substitutes traditional topology optimization algorithms, accelerates time-to-market, and is well-suited for applications with only minor variations in design conditions.

Keywords: Artificial neural network , Bidirectional evolutionary structural optimization (BESO) , Dataset analysis , Design for additive manufacturing , Frequency optimization , Topology optimization

Collaborative robotics is increasingly considered in manufacturing to improve efficiency while reducing operators physical and cognitive workloads. However, the lack of comprehensive methodologies has limited the consistent implementation of human–robot collaborative workstations across industries. Existing approaches are often fragmented, require robotics expertise, and pose challenges for non-experts, leading to suboptimal station designs and inefficient task allocation. This study presents a structured design framework to transition traditional assembly processes into collaborative ones. The framework provides a practical, scalable solution for optimizing collaborative workstations, balancing performance, ergonomics, and industrial applicability. It starts from the analysis of the assembly tasks, followed by classification and allocation between human operators and robots, and concludes with virtual prototyping and performance optimization through simulation using a commercial tool. The adopted methodology integrates task analysis, ergonomic assessment, and workspace design to ensure accessible and efficient implementation. Validated through two industrial case studies involving a gear pump and a worm gearbox, the approach demonstrated significant reductions in cycle time and notable improvements in the ergonomic working conditions. Additionally, physical prototyping and testing conducted within a research collaborative cell further confirmed the achieved results.

Keywords: Collaborative assembly , Collaborative robotics , Human–robot simulation , Integrated design framework , Task allocation , Virtual prototyping

Maintenance activities on transportation infrastructures, particularly on the underside of bridges and viaducts, present critical challenges related to safety, accessibility and cost-efficiency. Conventional solutions, such as scaffolding or mobile platforms, often exhibit substantial operational limitations. In this context, modular articulated robotic arms present a promising, safer, and more flexible alternative. This study introduces a functionality and data-driven design methodology for the modular development of articulated robotic arms specifically tailored for bridge maintenance applications. The proposed approach integrates Finite Element Analysis with clustering algorithms to identify mechanically equivalent joint modules based on their structural response under realistic loading conditions, including self-weight and external forces. Subsequently, topology optimisation techniques are applied to refine the geometry of selected joints, enhancing their stiffness-to-weight ratio while preserving manufacturability and modularity. Additionally, the application of Geometric Dimensioning and Tolerancing ensures mechanical reliability and precision during assembly. The effectiveness of the methodology is demonstrated through comparative Finite Element Analysis assessments between the initial and optimised designs, along with the construction of a functional prototype. The proposed framework paves the way for scalable, robust, and repeatable robotic solutions in the infrastructure maintenance sector.

Keywords: Bridge Maintenance , Clustering Algorithms , Geometric Dimensioning and Tolerancing , Modular Robotic Arms , Topology Optimisation

The development of multi-sensor perception systems is essential to support advanced underwater robotic operations such as inspection, mapping, and manipulation, typically carried out with Remotely Operated Vehicles (ROVs). However, field testing is often costly, logistically demanding, and constrained by safety and accessibility issues. This paper presents a flexible virtual testbed for the development and validation of perception pipelines in ROV-based missions. The framework integrates Unity game engine, for high-fidelity 3D simulation, with Robot Operating System 2 (ROS2), for real-time sensor processing, robotic control, and data exchange. The testbed provides a modular emulation of ROV architectures, integrating digital vehicle replicas, configurable underwater scenes, and simulated sensors such as stereo vision, forward-looking sonar, and inertial units. This setup enables the generation of synthetic but physically consistent data streams, including depth maps, acoustic imagery, and fused spatial reconstructions. An operator console developed in Unity integrates multiple Augmented Reality (AR) strategies to enhance situational awareness and manipulation accuracy. The proposed virtual testbed offers a cost-effective and flexible environment for iterative development, debugging, and operator training. It provides a valuable tool for accelerating the design and integration of perception systems in underwater robotics, especially in scenarios where field trials are impractical or high risk.

Keywords: Augmented Reality Visualization , Multi-Sensor Perception , Underwater Manipulation , Underwater Robotics

Neuroendoscopy treats intracranial pathologies through millimeter-scale channels using endoscopes introduced along a straight trajectory from a cranial entry point to the target. The entry point acts as a Remote Center of Motion (RCM), which must remain fixed to follow the surgical plan and avoid damage around the entry point. Existing robotic RCM platforms rely on rigid multi-link structures, increasing complexity and footprint. To mitigate these limitations, we propose a compact dual-joint compliant mechanism for neuroendoscopic manipulation. Building on the Tetra II flexure architecture, we redesigned and optimized the joint for neurosurgical use. The end-effector holder is moved from the central axis to the side to improve visual access, facilitate sterile draping and allow rapid instrument exchange while preserving the RCM constraint. The mechanical design targets directionally uniform stiffness in the working plane while minimizing parasitic RCM displacements. The mechanism uses two identical compliant joints in series, with the connection angle treated as a design variable. For each angle, the response is obtained by analyzing each joint separately in FEM and combining their contributions via rotation matrices. An angular offset of 300° yields near-isotropic stiffness, with a root-mean-square error of 0.90 N/m from an ideal isotropic behavior. A PA12 prototype was tested under 0.1±0.01 N radial loads. Experimental stiffness differed by ≤19% from FEM. The parasitic RCM displacement was 0.032 ± 0.018 mm for a 4.5°shaft rotation, well within the 1 mm neurosurgical tolerance. This dual-joint compliant RCM mechanism offers a practical alternative to conventional rigid-link designs.

Keywords: Compliant joint , Finite element methods , Minimally invasive surgery , Remote Center of Motion

The agricultural industry is undergoing a transformative shift as it seeks innovative solutions to meet the growing demands of modern, industrial-scale operations. Robotics is emerging as a key enabler, offering efficient and precise alternatives to traditional manual labor. While various soft and rigid grippers have been developed to this end, most rely either on complex control strategies or on oversimplified structures that fail to balance workspace, stress distribution, and manufacturability. To address this challenge, this paper presents the design, fabrication, and experimental validation of a sensorized, soft-pad-integrated compliant gripper for damage-free handling of agricultural produce. The gripper employs a compliant structure with differentiated soft pads to enhance grasp stability and contact friction. A novel methodology is introduced that integrates computer-aided engineering (CAE) tools with MATLAB for advanced analysis and genetic algorithm optimization of the compliant structure. COMSOL Multiphysics simulations further analyze the role of differentiated soft pads in distributing contact forces and resisting slippage. The gripper was fabricated using nylon for the compliant fingers, polylactide (PLA) for structural components, and Dragon Skin 30A silicone for the soft pads. Force-sensitive resistor (FSR) sensors were integrated to provide tactile feedback and regulate contact forces for safe handling. Experimental tests demonstrated that the gripper can robustly grasp a variety of produce—including lemons, zucchini, potatoes, cherry tomatoes, carrots, and onions—without causing damage.

Keywords: Agricultural robotics , Compliant mechanisms , Food/Fruit handling , Gripper , Soft pads

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

Many engineering applications require a constant operating force to safely interact with specific objects and prevent damage or destruction. This paper presents a compact constant-force mechanism (CFM) based on a novel architecture. The proposed design leverages the stiffness combination principle, integrating a negative-stiffness mechanism with a positive-stiffness element to achieve a near-constant force output. The negative stiffness is provided by a V-shaped bistable beam arranged in a parallel configuration, while the positive stiffness is contributed by a nested spring-like beam. As a fully compliant system, the mechanism benefits from advantages such as frictionless motion, cost-effectiveness, high precision, and scalability. These features make it well-suited for applications in soft robotic end-effectors, micro-electro-mechanical systems (MEMS), surgical tools, and biomedical devices. Finite element analysis (FEA) was employed for modeling, design, and validation of the mechanism's performance.

Keywords: Constant-Force Mechanism , Finite Element-based Design , Stiffness combination

Constant-force regulation is critical in applications ranging from precision micromanipulation to overload protection. This paper introduces a novel compliant constant-force mechanism (CCFM) realized with a single flexural V-shaped beam whose variable stiffness along its length produces the constant-force characteristic. Unlike traditional CCFMs that rely on stiffness-combination architectures or curved-beam flexures, the proposed design harnesses the intrinsic stiffness gradient of the V-shaped flexure to achieve a compact form factor akin to curved-beam devices. Owing to its simplicity and small footprint, this CCFM is ideally suited for robotic end-effectors, MEMS, micro-manipulation tasks, and other advanced applications. The mechanism's behavior has been modeled and validated through finite-element analysis (FEA).

Keywords: Constant-Force Mechanism , Finite Element-based Design , Variable Thickness

Progressive stiffness has been proposed as a solution to the common trade-off in the design of series elastic actuators: higher torque-tracking performance, achieved by stiffer springs, or safer physical interaction with the environment and enhanced torque transparency, offered by softer springs. In this work, we investigate the application of series elastic actuators with stiffening characteristic in the context of dynamic legged locomotion. A planar one-legged robot with an articulated leg is modeled and simulated using a multi-body dynamics framework, with a control strategy based on the Spring-Loaded Inverted Pendulum template. The actuators incorporate compact, monolithic compliant mechanisms based on flexure beams that exhibit a progressive torque deflection characteristic. The design of these mechanisms is parameterized in terms of length, thickness, and number of beams, and then optimized with the goal of reducing both the joints' torque requirements and the mechanical cost of transport during a running task. Simulations results show that, compared to stiff actuation and linear series elastic actuation configurations, properly tailored nonlinear stiffness profiles can improve actuation efficiency and reduce energy requirements in dynamic legged locomotion.

Keywords: Compliant Mechanism , Energy Efficiency , Legged Robot , Nonlinear Series Elastic Actuators , SLIP

Automating structural analysis processes in engineering is essential to keep pace with rapidly evolving design challenges. Manual simulation setups are not only time-consuming but also prone to errors, which can compromise both safety and efficiency. Automation allows for consistent, repeatable, and scalable analysis methods. This approach is important for optimising complex structures for performance and reliability under varying conditions. This paper explores the integration of Python scripting within ANSYS Workbench as a tool for automating structural analysis. By using Python's communication with the ANSYS ACT API and the CLR framework, the study demonstrates how engineers can define geometry, apply boundary conditions, and execute multiple simulation scenarios with minimal manual intervention. A detailed case study is presented, where external load data is imported from Excel, systematically applied to update load cases, and analysed to determine maximum equivalent stress values, all in an automated workflow. The approach not only streamlines repetitive processes but also significantly enhances the reliability and efficiency of finite element analyses. This study underscores the potential of scripting to improve simulation practices, important for advanced engineering optimisations.

Keywords: Ansys , Process Automation , Python , Structural Analysis

Lattice structure design is still dominated by strut-based forms and surface-based shapes, such as triply periodic minimal surfaces (TPMS), which both exhibit overlapping limitations. Strut lattices often show strong anisotropy because theirresponse depends heavily on cell orientation, while TPMS lattices are difficult to adjust when bounded by geometric constraints. These conditions eventually led to stagnation in the development of lattice morphology. Hybrid and topology optimization methods have appeared as possible alternatives, but many of them still produce modified versions of classical patterns. This study examined two lattice geometries: the Pyramorph, inspired by the shape of a pyramid, and the Topomorph, generated through a topology optimization framework. Both structures were designed using a CAD unit cell patterning technique and manufactured using the FDM method, with relative densities ranging from 0.40 to 0.44. Their mechanical behaviour was examined through FEA simulation and uniaxial compression testing. The parameter variations included cell orientations of 0°, 15°, 30°, and 45°, and cell sizes of 8 mm and 12 mm within a 24 mm specimen. The Topomorph showed superior strength, reaching 15–20 MPa, while the Pyramorph reached only 7–8 MPa. The highest value, about 20.5 MPa, was obtained from the Topomorph at 0° and with an 8 mm cell size. Failure modes indicated buckling and delamination in the Pyramorph, while the Topomorph tended to collapse progressively. These findings indicate that topology optimization combined with CAD-based patterning could significantly improve lattice performance.

Keywords: compressive behaviour , lattice structures , material engineering , topology optimization

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

The design and management of Industry X.0 manufacturing systems is based on several simulation tools and digital models, used to predict and optimize the final performance. The lack of a seamless interoperability between such tools requires the development of a Digital Thread, a communication framework able to connect and synchronize digital models with the physical assets throughout their lifecycles. This study presents a novel Digital Thread based framework for the simulation and Virtual Commissioning of robotic systems, built on a Docker infrastructure integrated with Hadoop 3.2.0. It leverages AutomationML (AML) as a data exchange standard and the Robot Operating System (ROS) for distributed robotic control. The proposed architecture supports offline programming and facilitates the deployment of custom, multi-brand robotic systems, while enabling seamless integration of AML-based Digital Twins. The proposed methodology is validated on a large scale robotic manufacturing cell for aircraft fuselage assembly, demonstrating its capability in terms of performance validation, operational coordination, and optimization of production sequences.

Keywords: AutomationML , Engineering Methods , ROS , Simulation Framework , Virtual Prototyping

Featured Application: The methodology presented in this work enables the direct extrusion-based 3D printing of soft, low-hardness RTV-2 silicone elastomers using industrial additive manufacturing systems. By adapting commercially available general-purpose silicones through viscosity tuning, the proposed approach expands the material palette for silicone additive manufacturing beyond proprietary formulations. This capability is particularly relevant for applications requiring highly compliant, biocompatible, and geometrically complex parts, such as soft robotics components, customized prosthetic and orthotic devices, medical simulation models, wearable interfaces, and flexible seals or gaskets. A particularly relevant industrial scenario for the proposed methodology is the small- to medium-batch production of customized silicone components, where traditional mold-based casting becomes economically inefficient due to tooling costs and long lead times. This study investigates the relationship between viscosity and manufacturability of two-component silicones in extrusion-based additive manufacturing. A methodology is proposed to adapt commercially available, low-viscosity general-purpose silicones for direct 3D printing using the material extrusion system provided by Lynxter S300X. EcoFlex™ 00-50 silicone was modified through controlled additions of a thixotropic agent (THI-VEX), producing formulations with progressively increased viscosity. After a preliminary qualitative viscosity assessment, formulations were printed using identical process parameters and evaluated through a set of dedicated geometric benchmark specimens targeting critical failure modes, including unsupported thin walls, overhangs, gaps, and slender structures. Print outcomes were assessed via multi-rater visual inspection with inter-rater reliability analysis to ensure consistency. Results reveal a strong correlation between thixotropy and geometric fidelity, identifying the formulation containing 4.0 wt% THI-VEX as optimal under the tested conditions. The study provides practical design and process guidelines for silicone additive manufacturing and highlights the importance of integrated material–process optimization for reliable fabrication of soft, highly deformable materials.

Keywords: geometric printability , material extrusion , RTV-2 silicone elastomers

This paper presents a comprehensive application of an automated surgical planning workflow for bone tumor resection, integrating two previously developed algorithms into a real clinical case. The study focuses on a patient with a left tibial osteosarcoma, demonstrating how the automated framework streamlines the entire preoperative process, from tumor resection planning to allograft selection. The cutting plane optimization tool reduced manual planning time from hours to just 19 min while ensuring complete tumor removal with minimal healthy bone resection. For reconstruction, the system automatically reconstructed the patient's premorbid anatomy and selected the best-matched allograft from a virtual donor database, achieving a high degree of anatomical precision (mean distance: 0.29 mm). The results highlight the clinical potential of this integrated approach, which improves efficiency, reduces subjectivity, and enhances reproducibility in complex bone tumor surgeries.

Keywords: Allograft , Design Method , Optimization , PSO , Tumor Resection , Virtual Prototyping

The growing use of wide-band-gap semiconductors in power electronics increases the demand for efficient yet low-cost thermal management. Pin-fin heat sinks offer a practical solution due to their robustness and ease of fabrication, but their geometric optimization via Computational Fluid Dynamics (CFD) models remains computationally demanding, particularly in multi-variable contexts. This work presents a novel surrogate modeling approach based on Non-Uniform Rational Basis Splines (NURBS) entities for predicting thermal and hydraulic performance in pin-fin cooling systems for traction inverters. Two CFD modeling strategies were explored: one allowing variable pin count, and another preserving topological consistency by adjusting the domain width. While the former introduced geometric discontinuities that degraded metamodel accuracy, the latter enabled the NURBS-based surrogate model to achieve high predictive performance with a few seconds evaluation times. Compared to Kriging surrogate models and Artificial Neural Networks (ANNs), the NURBS approach delivers superior computational efficiency, making it particularly well-suited for real-time applications, sensitivity analyses, and gradient-based optimization.

Keywords: Cooling systems , Metamodeling , NURBS , Surrogate Model , Power module

The transition to clean and sustainable transportation highlights the importance of hydrogen and fuel cell technologies as promising solutions for reducing greenhouse gas emissions. This paper presents the development and assessment of an adaptive frequency separation-based energy management strategy using fuzzy logic for fuel cell–battery hybrid vehicles. The studied system integrates a proton exchange membrane fuel cell as the primary source and a lithium-ion battery as the secondary source, both interfaced with the DC bus through DC/DC converters. A PI controller ensures DC bus voltage stability, while a sliding mode hysteresis controller provides robust regulation of the fuel cell current. Unlike conventional frequency separation methods, which only allocate low-frequency demand to the fuel cell and high-frequency demand to the battery, the proposed strategy introduces an adaptive correction mechanism based on the state of charge (SOC) of the battery and instantaneous load variations. This ensures efficient power allocation according to source dynamics while actively regulating the SOC within a safe operating range. Simulation results under UDDS and NEDC driving cycles confirm that the proposed strategy maintains the SOC between 40% and 80% with convergence toward the target value of 60%, while reducing the apparent hydrogen consumption by 30%. These improvements highlight the robustness and efficiency of the proposed strategy, making it a promising solution to extend the lifespan of hybrid energy sources in hybrid fuel cell electric vehicles.

Keywords: Energy management strategy , Frequency separation , Fuel cell hybrid electric vehicle , Fuzzy logic , Hydrogen , PI controller , Sliding-mode control