Geometric modeling and processing

Conventional CAD tools face challenges with lattice structures due to the geometrical complexity and numerous facets of STL digital models typically used in additive manufacturing workflows, prompting the need for alternative methods. This paper introduces a function representation-based approach to model uniform lattice structures using a 1D wireframe model followed by surface triangulation. The study highlights the efficiency of generating lattice structures using this method compared to other techniques available in the literature and traditional boundary representation. Unlike traditional methods, this approach removes the necessity for boundary representations of lattice structure models, resulting in more efficient data management. The findings of this research have significant implications for the development of lightweight 3D components optimized for additive manufacturing and their representation in CADs. This method is designed for industrial applications where rapid and efficient design of complex geometries is essential for achieving lightweight components.

Keywords: CAD modeling , Function Representation , Lattice structures , STL Triangulation

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

This work explores the automation of AutoCAD workflow using Python scripting, with a focus on the different Python libraries available for such tasks. The article analyzes four prominent Python packages—win32com, pyautocad, PyAutoGUI, and ezdxf—assessing their strengths and weaknesses in the context of AutoCAD automation. To evaluate the performance of these libraries, a Python application was developed for the simple task of prefixing layer names in an AutoCAD drawing, testing each library's effectiveness in accomplishing this task. The findings indicate that each Python library offers distinct advantages depending on the automation requirements, with ezdxf standing out in aspects such as versatility, robustness, and documentation quality.

Keywords: applied programming , AutoCAD , automation , computer aided design , Python

Temporomandibular joint (TMJ) prostheses aim to restore joint function in cases of severe dysfunction or anatomical loss. This study presents a subject-specific design methodology using an integrated CAD and multibody (MB) approach to evaluate and optimize TMJ prosthesis performance. Experimental mandibular motion was captured using an optical tracking system and applied to a MB model replicating the subject's anatomy. Three prosthesis designs were assessed: an existing design provided by a company (CD), a revised new design (ND) and a digged design (DD) created through contact-driven surface modification. Performance metrics included interincisal distance at mouth complete aperture, condyle displacement and TMJ contact forces. The CD exhibited premature condyle dislocation and limited mobility. The ND introduced anatomical stops and surface inclination, significantly improving range of motion and reducing asymmetry. The DD further enhanced performance through optimized articular surface shaping, achieving near-physiological kinematics and a 35.7% reduction in peak contact force compared to CD. Results suggest that prosthetic performance can be substantially improved by tailoring articular geometries to subject-specific motion data. Future work will extend the model to include musculature for more comprehensive biomechanical analysis.

Keywords: Mobility , Multibody Model , Reaction Forces , Stability , TMJ Design

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

Additive Manufacturing (AM) displays unique capabilities in terms of fabricating parts including unconventional geometries. Much research is conducted to investigate the performances of AM from both the mechanical and the quality viewpoint. This research is aimed to complete the transition of AM from a rapid prototyping technology to a mature manufacturing option. Interestingly, this research is routinely conducted on standard geometries instead of those kind of topologies for which AM is an advantageous fabrication system. The study presented here originally compares the accuracy of AM for standard and complex shapes. As a case study, we used the model of a leg of a quadruped robot obtained with generative design in previous research. The model includes standard and non-standard geometries, with the latter being predominant. Ten copies of this model have been produced by AM, specifically by means of a 3D printer working with Fused Deposition Modelling. Two different orientations have been used for the model (five copies each). The 3D-printed physical models have been 3D-scanned and compared with the reference CAD model, which has allowed us to evidence deviations. The deviations have been displayed by using both global and local best-fit options in a quality assessment software application. The first analysis of achieved data suggests that printing parameters and the position of surfaces affect the accuracy of AM more than the characterization of surfaces as standard and non-standard.

Keywords: Additive Manufacturing , Deviations , freeform , Generative Design

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

Mandibular and maxillary bone regeneration is a significant challenge in orthodontics and maxillofacial surgery, particularly in patients with bone atrophy or advanced periodontal diseases. This study presents an innovative approach that integrates advanced imaging and manufacturing technologies to design and fabricate customized scaffolds based on data obtained through Cone Beam Computed Tomography (CBCT). The objective is to create biomimetic bone scaffolds suitable for infusion with autologous stem cells, promoting functional bone regeneration. The workflow comprises three key stages. CBCT images are processed to reconstruct detailed three-dimensional models of the mandibular or maxillary structure. Then, using generative optimization algorithms and surface subroutines in commercial CAD software, scaffolds are designed to replicate the mechanical, geometric, and biological properties of natural bone. The scaffolds are fabricated using 3D printing techniques with biocompatible and biodegradable materials. 3D printing enables high precision in creating porous structures optimized for vascularization, cellular integration, and mechanical support. This multidisciplinary approach, combining medical imaging, CAD/CAM design, and additive manufacturing, offers an advanced solution to address the challenges of bone regeneration in orthodontics. Preliminary results suggest that infusing scaffolds with stem cells could significantly accelerate bone regeneration and improve integration with surrounding tissues. Future studies will focus on preclinical validation of scaffolds.

Keywords: Biocompatible material , Biodegradable material , Bone scaffold , DICOM images , Mandibular and Maxillary Reconstruction

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

Purpose: Reverse Shoulder Arthroplasty (RSA) is widely used to treat shoulder joint pathologies. However, this procedure may result in reduced range of motion (ROM), scapular notching, and prosthetic instability. These complications vary among patients, highlighting the need for individualized preoperative planning. This study introduces a novel parametric methodology to determine optimal glenoid component positioning by evaluating ROM, instability ratio, and the percentage of bone resected. Method: The proposed approach was applied to four patient models treated with two prosthetic designs. The methodology consists of four steps within a patient-specific parametric tool: 3D anatomical reconstruction, virtual surgical planning, biomechanical and geometric evaluation, and identification of optimal configurations. Fifteen glenoid component orientations were generated by varying tilt angles. The best configurations were identified based on ROM and instability assessments, while bone resection volume was calculated as an additional parameter. Results: Maximum values of abduction–adduction, internal rotation, and external rotation were 87.23°, 90°, and 70.59°, respectively, although not achieved in a single configuration. Instability ratios ranged from 0.23 to 0.62. Bone resection varied between 0.4% and 5.5%, depending on the configuration. Conclusions: This methodology provides a patient-specific framework to support preoperative planning in RSA.

Keywords: CAD-based planning , Glenoid positioning , Joint stability , Patient-specific modeling , Range of motion , Reverse shoulder arthroplasty

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

This study presents a genetic algorithm-based methodology for reconstructing the nominal profile of airfoils belonging to the NACA four- and five-digit series. By minimizing the geometric deviations between measured point clouds and parametrically generated airfoil profiles, the algorithm identifies the best-fitting nominal geometry. The approach was implemented using Rhino 8, Grasshopper, and the Galapagos plugin, and validated through extensive testing on 3D-printed samples. Across 200 test runs, the algorithm consistently identified the correct nominal geometry, demonstrating robustness despite inherent stochastic variability and computational challenges. The average number of iterations needed to converge was found to be 953 across all cases. This methodology offers a valuable tool for reverse engineering and metrological applications, providing a parametric and efficient alternative to traditional free-form surface reconstruction.

Keywords: Airfoil Design , Airfoil inspection , Genetic Algorithm , Reverse Engineering

This study presents a fully automated pipeline for the morphometric analysis of the dens, based on an enhanced Statistical Shape Model of the C2 vertebra. Developed in MATLAB, the pipeline integrates semantic segmentation directly into the shape model, enabling precise and reproducible identification of anatomical regions without manual intervention. Unlike deep learning approaches, the proposed framework is well-suited to applications involving limited or heterogeneous data. The model was trained on 25 C2 vertebrae from the VerSe dataset and tested on three independent specimens. Evaluation of model compactness, generalization, and specificity confirmed the robustness of the Statistical Model. Morphometric parameters are extracted in reference to a local coordinate system derived from the mean C2 shape, ensuring anatomical consistency across specimens. The morphometric results for the dens fell within the anatomical ranges reported in the literature, supporting the method's validity. While not intended for clinical use due to dataset limitations, this work lays the foundation for scalable, reproducible, and anatomically informed morphometric analysis of the dens.

Keywords: Automatic Feature Recognition , Biomedical Image Analysis , Statistical Shape Modeling

Accurate identification of spinal landmarks is critical in postural assessment, spinal deformity monitoring, and biomechanical modeling. Conventional techniques, such as radiographic imaging and manual palpation, suffer from significant limitations, including radiation exposure and operator dependence. This study presents a novel, fully automated method for localizing spinal apophyses using high-resolution 3D surface scans and shape index analysis. The proposed approach extracts the dorsal symmetry line and analyzes perturbations in the shape index, a differential geometric descriptor computed from local principal curvatures, to infer spinous process locations. Experimental validation was conducted on 21 healthy subjects using a structured-light 3D optical scanner, with palpation-based identification serving as a clinical reference. The method demonstrated high sensitivity, with a false negative rate of only 1.58% relative to operator-identified landmarks. The approach also identified additional plausible landmarks not detected by the operator, suggesting potential for improved diagnostic accuracy. These results support the method's suitability for objective, repeatable spinal landmark detection, with implications for both clinical assessment and computational modeling. Future work will focus on validation against radiographic ground-truth and applicability in pathological cases.

Keywords: 3D Surface Topography , Automated Landmark Detection , Shape Index Analysis , Spinous Process Detection

The maritime industry is looking for ways to decarbonize rapidly through design strategies, and technologies, also developing solutions for eco-friendly boating. The hydrofoil technology is one of the most promising due to its high efficiency compared to traditional planing or displacing vessels. The research was focused on the development of a systematic design method for fully submerged hydrofoil wing system with a longitudinal flight control. Starting from a reference hull model, five alternative wing configurations were designed based on geometric, hydrodynamic, and dynamic criteria. A preliminary hydrodynamic model was developed to determine the dimensions and positions of the wing systems in five different configurations. The equations of the motion were numerically implemented, and a longitudinal double PID flight control for the wing systems was developed. The configurations were designed benefiting from a parametric CAD model and simulated through transient CFD analysis at different speeds. In terms of drag, the lowest values were found for R4 (62 N) and R3 (64 N) during cruise. R1 and R0.75 were the best configurations in terms of following the flight pattern. Furthermore, at the same speed and displacement condition, the drag estimated for R4 was more than halved, if compared to the original model (130 N).

Keywords: Computational fluid dynamics , Double PID flight control system , Hydrofoil , Measurements , Parametric design and modelling

Traditional design reviews often rely on static inspection of 3D CAD models and physical prototyping, approaches that are typically time-consuming, costly, and limited in interactivity. While Virtual Reality (VR) offers a promising, immersive, and cost-effective alternative, its adoption in industry remains limited due to the limitations of commercial solutions and their poor integration with CAD environments. To overcome these challenges, this paper introduces a flexible, VR-based framework aimed at enabling an automated scene setup and immersive design reviews for early-stage assembly validation, before physical prototyping begins without the constraints of proprietary platforms. The framework is composed of two interconnected applications: (1) Model Setup Application, which facilitates the import and conversion of CAD models, along with the definition of assembly sequences enriched with technical and safety alerts, and (2) Immersive Design Review Application, which provides an interactive VR environment for navigating assemblies, validating procedures, and annotating key steps. This approach supports collaborative, virtual exploration of assembly processes, enhancing early design communication, reducing development lead times, and improving knowledge retention. The framework is compatible with consumer-grade VR hardware and supports standalone operation, making it a scalable and accessible solution.

Keywords: Assembly Validation , Immersive Design Review , Virtual Assembly , Virtual Reality

Home Cultivators (HCs) are emerging devices for food cultivation within residential environments, with increasing potential for self-sufficiency. Therefore, the current state of the art remains limited, with few established references. In this study, environmental simulations were conducted in Rhinoceros 3D and Grasshopper using Ladybug and Honeybee plugins (Radiance module) to assess the lighting and energy performance of a home cultivator oriented towards the south and north. Analyzed parameters include energetic variables (e.g., direct sunlight, cumulative radiation) and vegetative growth indicators (e.g., Photosynthetically Active Radiation, illuminance). This research project aimed to investigate several energy-related aspects concerning an HC and its cells in a NeoVernacular Kitchen context through computational simulations. To better understand the relationship between in-house lighting conditions and plant growth, a plugin named AiPlantcare was utilized to support the simulation process, enabling a more accurate assessment of light quality and energy efficiency. Results indicate that the south-facing side receives up to 44.9% more natural light and energy than the north, despite requiring shading strategies. In contrast, the north side offers greater stability but lacks sufficient resources for autonomous cultivation. CAD-based simulations provide a valuable tool for integrating cultivation systems into residential environments in the early design phase.

Keywords: Energy Efficiency , Energy Sustainability , Grasshopper , Home Cultivator , Rhinoceros 3D , Sustainable Design

Cutting guides are widely used in cranio-maxillofacial surgery by providing mechanical references for precise bone resections and reducing intraoperative variability. Nevertheless, their rigid and patient-specific design requires dedicated CAD modeling and fabrication, making them time-consuming to produce and difficult to adapt when anatomical conditions or bone surfaces change. This work presents a hybrid surgical cutting guide that combines a physically adjustable device with Augmented Reality (AR) feedback to support intraoperative alignment in maxillofacial osteotomies. The concept merges the tactile reliability of conventional guides with the adaptability of digital visualization, enabling surgeons to fine-tune the cutting plane directly through AR tracking. Registration was performed using cephalometric landmarks and an inside-out tracking approach with HoloLens 2, allowing precise superimposition of virtual cutting planes onto 3D-printed mandibular models. The system was evaluated by both expert and novice operators under three feedback conditions: no AR, holographic overlay, and real-time distance guidance. Results showed that AR feedback was associated with improved positional accuracy, with mean linear deviations of 1.27±0.71mm and angular errors of 4.46±3.27°. Operator experience influenced overall performance, yet enhanced feedback compensated part of this variability. Combining physical and digital guidance can yield more adaptable, precise, and reusable osteotomy tools, paving the way for flexible surgical assistance in clinical settings.

Keywords: Adjustable surgical guide , Augmented Reality , Cranio-maxillofacial surgery , Hololens 2 , Hybrid surgical guide , Pose estimation