Abstract: 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

Abstract: 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

Abstract: This paper presents a novel design-driven methodology for fabricating marine sandwich structures using additive manufacturing (AM). The approach leverages the geometric freedom enabled by Fused Filament Fabrication (FFF) to produce complex lattice cores from PA12-GF15, a fibreglass-reinforced nylon, directly integrated into sandwich panels with laminated glass fibre skins. A hull portion of the Chichester Scow dinghy was selected as a case study to demonstrate the method’s feasibility. The core geometry was generated through volumetric lattice modelling in Blender, enabling full customisation of the internal structure to match the outer hull surface. The resulting sandwich panel was produced entirely in-house and compared against traditional fibreglass laminates and aluminium-honeycomb-core panels. The results show that the 3D-printed sandwich structure achieves an approximate 58,7% weight reduction compared to solid fibreglass construction, while also offering improved manufacturing flexibility and potential for performance tuning through localised lattice control. This study highlights the potential of AM in marine engineering and lays the groundwork for future research on structural behaviour and large-scale implementation.

Keywords: Additive Manufacturing | Lattice structures | Lightweight design | Marine Engineering | Sandwich structures

Abstract: Purpose – Additive manufacturing enables complex geometries, but support structures still compromise surface quality, increase postprocessing effort and cause material waste. This study aims to evaluate the feasibility and process optimization of a dual-material support strategy using polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) on a single-head fused filament fabrication (FFF) system, with the objective of improving surface quality and support removability while preserving mechanical performance. Design/methodology/approach – An experimental campaign was conducted using PETG parts fabricated with PLA as an interfacial support material. Surface quality was assessed through roughness measurements, while mechanical performance was evaluated via tensile tests and digital image correlation. Manufacturing time, material consumption and ease of support removal were also quantified and compared with conventional single-material PETG printing. Findings – The introduction of a PLA interfacial layer significantly improved surface quality and support removal. Surface roughness anisotropy was reduced by up to 99% in vertically printed specimens and 86% in horizontally printed ones compared to single-material PETG. Supports were easily removed without surface damage. Ultimate tensile strength decreased by approximately 20% in vertical specimens and 49% in horizontal specimens, whereas the secant modulus showed limited variation (<10%). Manufacturing time and material usage increased by 57% and 35%, respectively, due to material purging during switching. Finally, the force required to remove the supports completely dropped by 63% on average from the single-material to the dissimilar-material configuration. Practical implications – The proposed approach is suitable for prototypes and functional parts where surface quality and dimensional consistency are critical, especially when only single-nozzle machines are available. Originality/value – This study demonstrates a practical and effective multimaterial support strategy using dissimilar polymers on a single-head FFF machine, offering an alternative to soluble supports and multiextruder systems.

Keywords: Dissimilar materials | Fused filament fabrication (FFF) | Multimaterial | Support optimization | Surface quality

Abstract: Fused filament fabrication (FFF) is one of the additive manufacturing methods used to transform digital models cost-effectively into prototypes, mockups, and functional parts for industrial customized applications, mainly aerospace, automotive, and biomedicine. In an industrial standard design-to-manufacturing workflow, the slicing software is responsible for translating the digital model of the object into a set of instructions for the FFF machine. However, setting printing profiles for FFF machines is a painstaking process in the operative environment due to the long time needed to carry out the required tests and tuning phases. Moreover, the scientific literature needs to include the influence of digital model topologies on the more influencing manufacturing parameters. Thus, this paper proposes a reproducible methodology to understand how the choice of the manufacturing parameters affects the time estimation and mass of the production process. Through a half-factorial Design of Experiment approach, the manufacturing parameters that most significantly affect the time required are identified; furthermore, the methodology aims to suggest adjustments to enhance the accuracy of build time predictions in commercial slicing software. Several case studies in the paper provide empirical support for the findings, highlighting that proper configuration of commercial slicing software can substantially enhance manufacturing process accuracy. In particular, the results show that the best configuration cannot be chosen a priori since the topology of the component affects the optimal choice of parameters. Moreover, a rigorous statistical approach allows for producing functional components with excellent printing times and optimal material consumption, compared to a more random approach that may lead to non-functional components. The methodology suits the industrial environment where processes must be set up quickly with satisfying results.

Keywords: Cost | Design of experiment (DoE) | Fused filament fabrication (FFF) | Manufacturing time | Printing parameters | Slicing

Abstract: This paper presents a scalable, open-source method for designing strut-and-node lattice structures for industrial applications, including uniform and graded lattices. Traditional computer-aided design tools struggle with lattice structures with high complexity, prompting the need for alternative approaches. While function representation techniques are commonly applied to triply periodic minimal surface lattices, their use for strut-and-node lattices has been limited. The proposed method defines the unit cell geometry using function representation primitives to model cylindrical struts and spheres, followed by isosurface triangulation and spatial replication within a voxelized design space. To showcase its practical application, two case studies are presented in which industrial components are filled with uniform and graded lattice structures using the newly developed model. The paper includes a comprehensive analysis of the computational cost of the approach. Furthermore, the study evaluates the geometric accuracy and quality of the generated lattice, highlighting their suitability for lightweight design in additive manufacturing. This method eliminates the need for boundary representations of lattice structure models, leading to more efficient data handling. The results of this research have broad implications for developing 3D components optimised for additive manufacturing. The approach targets industrial use, enabling fast, efficient design of complex, lightweight geometries.

Keywords: additive manufacturing | CAD file format | computer-aided design | Lattice structures | lightweight design

Abstract: Structural topology optimization approaches are widely used to create lightweight and efficient components through additive manufacturing. The race to lightweight components should also account for natural frequencies when designing components and structures subjected to dynamic loads, as in aerospace and automotive engineering. These optimization tools require extensive manual setup, mainly when tuning input parameters that govern algorithmic functions and convergence. In this context, machine learning approaches can circumvent the trial-and-error process associated with the manual setup of simulation factors. This study presents a method that utilizes machine learning to suggest optimal input parameters, such as evolutionary rate and mesh dimensions, for creating lightweight and optimized structures while maintaining a consistent natural frequency. The framework incorporates a neural network trained on a collection of previously solved, analogous problems. A dissimilarity metric derived from problem metadata is used to determine tuning parameters. This approach can be applied to analyze and optimize product configurations where only marginal conditions may change; a Bayesian optimizer based on data coming from the neural network is used to improve the structure, substituting the topology optimization algorithm and reducing the time-to-market of a specific product.

Keywords: Artificial Neural Network | Bidirectional Evolutionary Structural Optimization (BESO) | Design for Additive Manufacturing | Frequency Optimization | Topology Optimization

Abstract: The topology optimization methodology is widely utilized in industrial engineering for designing lightweight and efficient components. In this framework, considering natural frequencies is crucial for adequately designing components and structures exposed to dynamic loads, as in aerospace or automotive applications. The scientific community has shown the efficiency of Bi-directional Evolutionary Structural Optimization (BESO), showcasing its ability to converge towards optimal solid-void or bi-material solutions for a wide range of frequency optimization problems in continuum structures. However, these methods show limits when the complexity of the domain volume increases; thus, they are well-suited for academic case studies but may fail when dealing with industrial applications that require more complex shapes. The connectivity of the structures resulting from the optimization also plays a fundamental role in choosing the best optimization approach, as some available commercial and open-source codes nowadays return unfeasible sparse structures. An improved voxel-based BESO algorithm has been developed in this work to cope with current limits in lightweight structure optimization. A significant case study has been developed to evaluate the performances of the new methodology and compare it with existing algorithms. In contrast to previous studies, the method we developed guarantees that the final structure respects constraints on the initial design volume and that the structure’s connection is preserved, thus enabling the manufacturing of the component with Additive Manufacturing technologies. The proposed approach can be complemented by smoothing algorithms to obtain a structure with externally appealing surfaces.

Keywords: Additive manufacturing | Bi-directional evolutionary structural optimization (BESO) | Natural frequency | Optimal design | Topology optimization

Abstract: This study aims to evaluate the advantages and criticalities of applying additive manufacturing to produce climbing holds replicating real rocky surfaces. A sample of a rocky surface has been reproduced with a budget-friendly 3D scanner exploiting structured light and made in additive manufacturing. The methodology is designed to build a high-fidelity replica of the rocky surface using only minor geometry modifications to convert a 2D triangulated surface into a 3D watertight model optimised for additive manufacturing. In addition, the research uses a novel design and uncertainty estimation approach. The proposed methodology proved capable of replicating a rocky sample with sub-millimetre accuracy, which is more realistic than conventional screw-on plastic holds currently used in climbing gyms. The advantages can be addressed in terms of customisation, manufacturing cost and time reduction that could lead to real outdoor climbing experiences in indoor environments by coupling additive manufacturing techniques and reverse engineering (RE). However, operating the scanner in a rocky environment and the considerable size of the climbing routes suggest that further research is needed to extend the proposed methodology to real case studies. Further analysis should focus on selecting the best material and additive manufacturing technology to produce structural components for climbing environments.

Keywords: 3D scanning | Additive manufacturing | Climbing holds | Fused deposition modelling | Reverse engineering | Uncertainty quantification

Abstract: Over the past few decades, the scientific community’s and industry’s interest in additive manufacturing technologies has surged. This technology is distinguished by the layer-by-layer deposition of the raw materials and the piece’s growth in a predetermined build orientation. This factor impacts the process’ overall cost, surface quality, and other crucial parameters. Numerous methods to solve competing aspects have been proposed in the literature, with the more promising that iteratively uses ray-tracing techniques. Existing algorithms iterate for each discrete element of the model’s bounding box projection onto the building platform. However, when optimisation algorithms are used on real-life industrial parts, computational time problems arise due to the high number of faces in the models. A new computational technique to determine the appropriate part orientation to reduce the support volume is proposed to address the problem. The method reduces the computational time, cycling the ray-tracing only on the triangles where the model surface is discretised. This approach has been integrated into an enhanced particle swarm optimisation algorithm to prove its efficiency. The approach is intended for industrial applications where it is necessary to handle complicated geometries quickly and efficiently to find the best orientation. Based on the computer’s resources and the complexity of the faceted model, a set of case studies with an industrial engineering significance is used to demonstrate the approach’s effectiveness.

Keywords: Additive manufacturing | Build orientation | Part orientation | Particle swarm optimisation | Support material

Abstract: Additive Manufacturing is widely applied in aerospace, automotive and marine engineering. Indeed, large-scale components are often required in these applications, such as for non-structural parts of aircraft, spare parts or small lots of cars or marine components. Fused Deposition Modelling is one of the Additive Manufacturing processes used to affordably convert digital models into mockups, prototypes, and functional parts: a slicing software converts the object’s digital model into a list of instructions for the machine. However, commercial slicing software packages often fail to accurately estimate the time required to produce models, especially when their size is significant: the errors could be up to several hours, which cannot be adequate in a real-life industrial context where production must be scheduled in a precise way. This manuscript compares the build time estimation of several commercial slicing software considering a real-life part. Furthermore, the evaluation of the manufacturing setting mainly affects the error in estimating the build time achieved through a Design of Experiment approach. The more time-impacting printing parameters have been detected, allowing fine helpful tuning to increase the accuracy of the build time in commercial slicing software. A case study included in the manuscript supports the analyses. Proper setting of the commercial slicing software can significantly improve the accuracy of the printing time.

Keywords: Additive Manufacturing | Design of Experiment | Fused Deposition Modelling | Slicing | Time Estimation

Abstract: Natural disasters have a significant effect in terms of impacted individuals and casualties. Artificial Intelligence (AI) techniques for automatically segmenting landslides from aerial photos is a relatively new field of research. Segmenting landslips quickly and accurately can significantly aid in assessing the damage caused by natural disasters. This research aims to compare the performance of AI techniques with more classical methods for the automatic segmentation of landslides from aerial images for damage assessment. It is presented a dataset of satellite images containing landslides collected in the Broni (Italy) region and annotated to train and test the segmentation model. Both classical image processing techniques, such as thresholding and edge detection, and AI-based methods, such as U-Net, are applied to the dataset. Overall, this research demonstrates that AI-based methods are a promising tool for automatically segmenting landslides from aerial images and can be a powerful asset in assessing the damage caused by natural disasters. The study also highlights the importance of combining classical and AI-based methods for better performance, especially in challenging and complex scenes.

Keywords: Artificial Intelligence | Damage Assessment | Landslips | Semantic Segmentation

Abstract: Purpose: Timing constraints affect the manufacturing of traditional large-scale components through the material extrusion technique. Thus, researchers are exploring using many independent and collaborative heads that may work on the same part simultaneously while still producing an appealing final product. The purpose of this paper is to propose a simple and repeatable approach for toolpath planning for gantry-based n independent extrusion heads with effective collision avoidance management. Design/methodology/approach: This research presents an original toolpath planner based on existing slicing software and the traditional structure of G-code files. While the computationally demanding component subdivision task is assigned to computer-aided design and slicing software to build a standard G-code, the proposed algorithm scans the conventional toolpath data file, quickly isolates the instructions of a single extruder and inserts brief pauses between the instructions if the non-priority extruder conflicts with the priority one. Findings: The methodology is validated on two real-life industrial large-scale components using architectures with two and four extruders. The case studies demonstrate the method\'s effectiveness, reducing printing time considerably without affecting the part quality. A static priority strategy is implemented, where one extruder gets priority over the other using a cascade process. The results of this paper demonstrate that different priority strategies reflect on the printing efficiency by a factor equal to the number of extrusion heads. Originality/value: To the best of the authors’ knowledge, this is the first study to produce an original methodology to efficiently plan the extrusion heads\' trajectories for a collaborative material extrusion architecture.

Keywords: Additive manufacturing | Collaborative manufacturing | FDM | MEX | Multiple heads | Toolpath planning

Abstract: In situations requiring high levels of customization and limited production volumes, additive manufacturing (AM) is a frequently utilized technique with several benefits. To properly configure all the parameters required to produce final goods of the utmost quality, AM calls for qualified designers and experienced operators. This research demonstrates how, in this scenario, artificial intelligence (AI) could significantly enable designers and operators to enhance additive manufacturing. Thus, 48 papers have been selected from the comprehensive collection of research using a systematic literature review to assess the possibilities that AI may bring to AM. This review aims to better understand the current state of AI methodologies that can be applied to optimize AM technologies and the potential future developments and applications of AI algorithms in AM. Through a detailed discussion, it emerges that AI might increase the efficiency of the procedures associated with AM, from simulation optimization to in-process monitoring.

Keywords: Additive manufacturing | Artificial intelligence | Deep learning | Machine learning | Optimization | Review

Abstract: Industrial engineering applications often require manufacturing large components in composite materials to obtain light structures; however, moulds are expensive, especially when manufacturing a limited batch of parts. On the one hand, when traditional approaches are carried out, moulds are milled from large slabs or laminated with composite materials on a model of the part to produce. In this case, the realisation of a mould leads to adding time-consuming operations to the manufacturing process. On the other hand, if a fully additively manufactured approach is chosen, the manufacturing time increases exponentially and does not match the market’s requirements. This research proposes a methodology to improve the production efficiency of large moulds using a hybrid technology by combining additive manufacturing and milling tools. A block of soft material such as foam is milled, and then the printing head of an additive manufacturing machine deposits several layers of plastic material or modelling clay using conformal three-dimensional paths. Finally, the mill can polish the surface, thus obtaining a mould of large dimensions quickly, with reduced cost and without needing trained personnel and handcraft polishing. A software tool has been developed to modify the G-code read by an additive manufacturing machine to obtain material deposition over the soft mould. The authors forced conventional machining instructions to match those of an AM machine. Thus, additive deposition of new material uses 3D conformal trajectories typical of CNC machines. Consequently, communication between two very different instruments using the same language is possible. At first, the code was tested on a modified Fused Filament Fabrication machine whose firmware has been adapted to manage a milling tool and a printing head. Then, the software was tested on a large machine suitable for producing moulds for the large parts typical of marine and aerospace engineering. The research demonstrates that AM technologies can integrate conventional machinery to support the composite materials industry when large parts are required.

Keywords: additive manufacturing | aerospace engineering | Fused Filament Fabrication | G-code | hybrid manufacturing | marine engineering | mould

Abstract: Nowadays, Search and Rescue operations can be performed using manned or unmanned Aerial Vehicles. In this latter case, compact cameras are mounted onboard and a bird’s eye view is available to find the missing person. However, the analysis of the video frames can be very challenging and dull for the operators. In this context, the use of graphical methodologies can boost the searching operations and improve the process. In this study, a methodology based on the object detector Yolov5 is introduced: the performances in detecting small objects such as persons in aerial images are evaluated. These algorithms implement shallow layers of the feature extractor to increase the spatial-rich features and help the detector to find small objects. Finally, detection algorithms are tested using a video simulating a scenario for Search and Rescue operations. The filtering of frames containing false positives, is carried out using a classical graphical tool such as the Hamming distance.

Keywords: Aerial images | Graphical methodologies | Image analysis | Object detection | SAR operations

Abstract: Additive Manufacturing (AM) is continuously increasing its appeal as a breakthrough production process due to well-established advantages compared to traditional manufacturing strategies based on chip removal or casting. The design of lightweight structures can exploit the AM advantages, thanks to the capability of shaping complex geometries where the constant level of stress can be achieved through Topology Optimization. Moreover, in transportation engineering and lightweight structures in general, thin-shell or thin-walled components are widely used for frames, fuselages, wings, car bodies, coaches, tanks or recipients. However, the application of topology optimization routines on thin-walled structures is not exempt from difficulties. This is true especially in the case of a distributed pressure load coming from fluid-structure interaction analysis. Coupling the benefits of TO methodology with the already good performances of thin-walled structures may lead to mechanically efficient shapes. This research addresses strategies to apply topology optimization on thin-walled structures. The effect of the local concentration of distributed load in a cloud of control points distributed along the surface of interest is considered and tested. Two case studies coming from industrial engineering have been carried out to show the capabilities of the proposed approach.

Keywords: Additive Manufacturing | Design for Additive Manufacturing | Distributed load | Thin-walled structure | Topology Optimization

Abstract: Lightweight bioinspired structures are extremely interesting in industrial applications for their known advantages, especially when Additive Manufacturing technologies are used. Lattices are composed of axial elements called ligaments: Several unit cells are repeated in three directions to form bodies. However, their inherent structure complexity leads to several problems when lattices need to be designed or numerically simulated. The computational power needed to capture the overall component is extremely high. For this reason, some alternative methodologies called homogenization methods were developed in the literature. However, following these approaches, the designers do not have a local visual overview of the lattice behavior, especially at the ligament level. For this reason, an alternative mono-dimensional (1D) modeling approach, called lattice-to-1D is proposed in this work. This method approximates the ligament element with its beam axis, uses the real material characteristics, and gives the cross-sectional information directly to the solver. Several linear elastic simulations, involving both stretching and bending dominated unit cells, are performed to compare this approach with other alternatives in the literature. The results show a comparable agreement of the 1D simulations compared with homogenization methods for real tridimensional (3D) objects, with a dramatic decrease of computational power needed for a 3D analysis of the whole body.

Keywords: homogenization | lattice structure | periodic structure | structural analysis | voxel

Abstract: The interest of industrial companies for the Additive Manufacturing (AM) technology is growing year after year due to its capability of producing components with complex shapes that fit industrial engineering necessities better than traditionally manufactured parts. However, conventional Computer-Aided Design (CAD) software are often limited for the design and representation of complex geometries, especially when dealing with lattice structures: these are bio-inspired structures composed of repeated small elements, called struts, which are combined to shape a unit cell that is repeated across a domain. This design method generates a lightweight but stiff component. The scope of this work is to analyse the problem of the lattice structures representation in 2 D technical drawings and propose some contributions to support the development of Standards for their 2 D representation. This work is focused on the proposal of rules useful to represent such hierarchic structures. Python language and the open-source software FreeCad™ are used as a software platform to evaluate the suitability and usability of the proposed representation standard. This is based on simplified symbols to describe complex lattice structures instead of representing all the elements which constitute the lattice. The standard is thought to be used in technical 2 D drawings where assemblies are represented and lattice components are used (e.g. parts assembly, maintenance, parts catalogues). A case study is included to describe how the proposed standard could be integrated into a 2 D assembly drawing, following technical product documentation production typical workflow.

Keywords: Additive manufacturing | design | drawing standards | ISO standards | lattice structures

Abstract: The Panel method is an approach for the estimation of the lift of 3D models which is faster than CFD. This can be useful especially in the conceptual design stage where several configurations should be evaluated in a reduced time with a limited computational cost. However, the meshing of the 3D body surface with rectangular panels can be a time-consuming activity because the designer should define from scratch a cloud of points that matches the external surfaces of the tested object to obtain consistent panelling. Therefore, a voxelization-based methodology has been developed to obtain the panels’ position, speeding up and automating the model preparation process. The obtained discretization has been integrated into a panel method available in the literature. Four case studies, of increasing complexity, have been analyzed to investigate the capability of the innovative voxel-based panel methodology. A parametric study has been carried out to study the effect of the voxel grid dimension on the accuracy of the results. Benchmarking values of lift coefficient obtained from literature or xFoil software have been used to evaluate the precision that can be achieved with this approach. The results show a good agreement between the voxel-based panel method and the literature when the overall pressure distributions and aerodynamic coefficient values are considered. Higher errors are noticed with drag.

Keywords: Fluid dynamics | Panel method | Potential flow | Voxelization

Abstract: The panel method is a potential-flow numerical approach that shows valuable performances to solve aerodynamic problems in the preliminary design stages. It shows a lower computational effort compared with Computational Fluid Dynamics, wind tunnel tests or ‘on the field’ experiments. However, the 3D surface discretization in rectangular panels is tedious and must be often carried out manually from scratch. Moreover, the panel method can’t be used to compute the overall drag force due to strong assumptions. To solve these two challenging aspects, the authors propose a voxel-based fluid dynamic approach integrating its programmed functions within a panel method. Voxelization is used to automatically distribute coherently the panels along the external surface of a 3D model in an automated way. A parametric study is included to demonstrate how the voxel resolution affects the aerodynamic results and provide guidelines for future research. Overall drag is estimated using corrections for both the skin friction and the form drag sources. The Ahmed body case study is included and demonstrates a good agreement between the voxel-based fluid dynamics approach and the literature benchmarking values, but with lower computational efforts. Further studies involving more complex shapes should be performed to better understand the performances and limitations of the approach.

Keywords: Ahmed body | Automotive | CAD | Conceptual Design | Panel method | Voxelization

Abstract: Well-established advantages as design freedom, acceleration of design-to-manufacturing cycle, decreased internal logistics reflect on the wider application of Additive Manufacturing as the main manufacturing process. However, its application to large-scale components manufacturing is still an open challenge, because of the limited printing volume available in off-the-shelf machines, slow manufacturing process, and low production volume. After a review of the available contributions, this paper proposes a methodology to handle large-scale 3D models, to be applied before the slicing process. The methodology is based upon the large-scale component subdivision into subparts within CAD environments, using an innovative approach tailored to the problem, and exploits the multi-head capability of collaborative large-scale AM machines. A UAV fixed-wing shows the positive effects in terms of speeding up the manufacturing process. The approach can significantly reduce the printing time of large parts, but a new generation of Additive Manufacturing machines is required to exploit the methodology.

Keywords: Additive Manufacturing | Aerospace | Automotive | Collaborative Manufacturing | Large-Scale Part | Multi-head Extruder

Abstract: In the last decades, the flourishing of Additive Manufacturing (AM) promoted innovative design solutions in many different sectors. Despite the numerous advantages of AM technology, there are still open challenges in the field. In Fused Deposition Modelling (FDM) structures the layer-by-layer manufacturing process induces anisotropy in the material properties of the structures. The correct characterization of the mechanical properties is fundamental in the design and development stages but at the same time difficult to achieve. The experimental approach can be extremely long and expensive. An alternative is the use of an accurate numerical approach and performing a Finite Element Analysis (FEA) of the geometry which is effectively printed. However, to the best of the authors\' knowledge, there is not a common and well-established procedure to reconstruct the real geometry which is generated after the slicing process. In this paper, starting from the information provided by the G-CODE, an easy-to-use, and reproducible methodology to reconstruct the printed geometry is presented. The performance of the innovative approach is evaluated via qualitative observations by referring to several case studies. The results are thoroughly analysed, and future trends and research needs are highlighted.

Keywords: Additive Manufacturing | CAD | Fused Deposition Modelling | G-CODE

Abstract: The topology optimization methodology is widely applied in industrial engineering to design lightweight and efficient components. Despite that, many techniques based on structural optimization return a digital model that is far from being directly manufactured, mainly because of surface noise given by spikes and peaks on the component. For this reason, mesh post-processing is needed. Surface smoothing is one of the numerical procedures that can be applied to a triangulated mesh file to return a more appealing geometry. In literature, there are many smoothing algorithms available, but especially those based on the modification of vertex position suffer from high mesh shrinkage and loss of important geometry features like holes and surface planarity. For these reasons, an improved vertex-based algorithm based on Vollmer’s surface smoothing has been developed and introduced in this work along with two case studies included to evaluate its performances compared with existent algorithms. The innovative approach herein developed contains some sub-routines to mitigate the issues of common algorithms, and confirms to be efficient and useful in a real-life industrial context. Thanks to the developed functions able to recognize the geometry feature to be frozen during the smoothing process, the user’s intervention is not required to guide the procedure to get proper results.

Keywords: Additive manufacturing | Mesh processing | Structural manufacturing | Surface smoothing | Topology optimization

Abstract: Nowadays additive manufacturing is affected by a rapid expansion of possible applications. It is defined as a set of technologies that allow the production of components from 3D digital models in a short time by adding material layer by layer. It shows enormous potential to support wind musical instruments manufacturing because the design of complex shapes could produce unexplored and unconventional sounds, together with external customization capabilities. The change in the production process, material and shape could affect the resulting sound. This work aims to compare the music performances of 3D-printed trombone mouthpieces using both Fused Deposition Modelling and Stereolithography techniques, compared to the commercial brass one. The quantitative comparison is made applying a Design of Experiment methodology, to detect the main additive manufacturing parameters that affect the sound quality. Digital audio processing techniques, such as spectral analysis, cross-correlation and psychoacoustic analysis in terms of loudness, roughness and fluctuation strength have been applied to evaluate sounds. The methodology herein applied could be used as a standard for future studies on additively manufactured musical instruments.

Keywords: Additive manufacturing | Design of experiment | Musical instruments | Sound analysis | Stereolithography

Abstract: This paper describes a methodology to design and optimize a controllable pitch propeller suitable for small leisure ship boats. A proper range for design parameters has to be set by the user. An optimization based on the Particle Swarm Optimization algorithm is carried out to minimize a fitness function representing the engine’s fuel consumption. The OpenProp code has been integrated in the procedure to compute thrust and torque. Blade’s geometry and tables about pitch, thrust and consumption are the main output of the optimization process. A case study has been included to show how the procedure can be implemented in the design process. A case study shows that the procedure allows a designer to sketch a controllable pitch propeller with optimal efficiency; computational times are compatible with the design conceptual phase where several scenarios must be investigated to set the most suitable for the following detailed design. A drawback of this approach is given by the need for a quite skilled user in charge of defining the allowable ranges for design parameters, and the need for data about the engine and boat to be designed.

Keywords: CAD | Controllable pitch propeller | Design propeller | Particle swarm optimization

Abstract: Smoothing algorithms are used for mesh refinement and to remove undesired surface. This numerical procedure is recommended and applied on triangulated file coming from 3D scanners or Topology optimization designs based on voxel representation before the optimized structure is manufactured by Additive Manufacturing technologies. In literature, there are several available algorithms, but many of them suffer from mesh shrinkage and do not give to the designer easy procedures to select regions which do not need the application of the smoothing procedure as holes or flat surfaces. For this reason, an improved vertex-based algorithm is presented in this work along with a case study to prove its performances compared with existent algorithms. The algorithm confirms to be efficient and useful. However, user\'s intervention is required to guide the procedure to get proper results.

Keywords: mesh fairing | mesh smoothing | topology optimization | voxel