Additive manufacturing

PolyEther Ether Ketone (PEEK) is a leading thermoplastic biomaterial renowned for its exceptional overall properties and suitability for processing via Fused Filament Fabrication (FFF) Additive Manufacturing (AM). Despite its widespread use in industries, its fatigue behavior remains a critically underexplored aspect. This study aims to investigate the fatigue behavior of FFF-produced PEEK, considering variations in infill pattern (triangular or rectilinear) and layer height (0.15 mm or 0.25 mm). Fatigue tests revealed a significant influence of the infill pattern on fatigue behavior, with the rectilinear pattern outperforming the triangular one. Layer height had a negligible effect when paired with rectilinear infill. Specimens with rectilinear patterns exhibited superior fatigue performance, achieving infinite fatigue life at maximum applied stress around 70% of ultimate tensile stress. Surface roughness assessment and fracture surface analysis at scanning electron microscope enhanced the interpretation of the results. This pioneering study lays the groundwork for future research in design for AM.

Keywords: additive manufacturing , fatigue tests , fused filament fabrication , mechanical properties , PEEK , surface roughness

Purpose: This paper aims to investigate how the build orientation simultaneously affects the tensile properties, geometrical measurements and surface roughness in material extrusion (MEX) produced parts. Design/methodology/approach: An extensive experimental campaign was designed and carried out to elucidate the relationship between the rotation angles (input), defining the part orientation within the build volume, and the (output) variables measured by using 3D models reconstruction, roughness tester and tensile testing machine. Response surface methodology is used to capture the trend of each output relative to the input, while principal component analysis is used to identify relationships among outputs, providing a holistic understanding of how build orientation simultaneously influences mechanical properties, geometrical measurements and surface characteristics. Findings: The study reveals that build orientation significantly affects nearly all output variables, with a pronounced dependency on the out-of-plane rotation angle. A key finding is the inverse correlation between mechanical strength and both geometrical measurements and surface roughness. This indicates that optimizing build orientation can enhance mechanical strength while minimizing geometrical defects. Originality/value: This research, a newer addition to the existing literature, contributes to the field of additive manufacturing (AM) by offering an innovative analysis of the interaction between mechanical properties, geometric precision and surface roughness in relation to build orientation. It enhances the understanding of MEX processes and provides valuable insights into optimizing build orientation, thereby improving the competitiveness of AM over traditional production methods.

Keywords: Build orientation , Geometrical measurements , Material extrusion , Surface roughness , Tensile properties

The multi-scale asymptotic method provides a separate description of the viscous and thermal response functions governing acoustic waves propagation through porous materials. However, these response functions are inherently interdependent for simple porous structures such as slits or tubes – their determination being directly influenced by the microstructural features of the geometry. This study aims to identify, characterize, and realize a microgeometry providing the ability to independently modify the viscous and thermal behaviours. A relative autonomy in the control of these phenomena would make it possible to regulate energy conversion processes within the structure, which can be either dissipative (e.g., sound absorption) or generative (e.g., thermoacoustic gain). Among the various microgeometries, cellular solids made by an assembly of cells with solid edges or faces, packed together so that they fill space, emerge as promising candidates for achieving such a goal. Within the interconnected cells (pores), the viscous losses are governed by the aperture sizes of the faces (throat section). On the other hand, the thermal exchanges are closely related to the interface between the fluid and the solid and consequently to the dimensions of the cells. The identified microstructure is a typical Kelvin cell-based geometry, modified to account for manufacturing constraints, and characterized by faces with well-defined opening ratio and thickness. This work presents a model, experimentally validated, to predict the transport parameters of such cellular solids. It provides a valuable tool for designing microstructures having the attributes to independently tune thermal and viscous effects for specific application requirements.

Keywords: Additive manufacturing , Cellular structure , Design methods , Energy control , Transport parameters

Purpose: This study aims to integrate design methods and additive manufacturing with the use of a thermoplastic elastomer certified for medical use and reverse engineering towards a new concept of a customized buttress model with optimized features for the reconstruction of the osteo-dural opening after endoscopic endonasal transtuberculum-transplanum approach. Design/methodology/approach: Additive manufacturing allows making of cost-effective and useable devices with tailored properties for biomedical applications. The endoscopic endonasal approach to the suprasellar area enables the management of different intradural tumours, and the craniectomy at the skull base is generally wide and irregular. Defining an optimal strategy for osteodural defect closure at the preoperative stage represents a significant challenge. Findings: Using the results obtained from a computed tomography analysis, skull base defects were designed to plan the surgical approach. Several concepts of customized buttress models were first built up, initially focusing on thin, flexible edges characterized by different thicknesses. Finite element analyses and design optimization allowed us to achieve the optimal design solution with improved compliance/flexibility for easy intranasal manoeuvrability, maintaining an adequate mechanical stability. As the thickness of the edges decreased, an increase of strain energy values was found (i.e. 1.2 mJ – Model A, 1.7 mJ – Model B, 2.3 mJ – Model C, 4.3 mJ – Model D). However, a further optimization (Model E) led to a significant increase of the compliance (strain energy of 14.1 mJ). Originality/value: The results obtained from clinical evaluations demonstrated the feasibility of the proposed technical solutions, improving surgery effectiveness.

Keywords: Design for additive manufacturing , Endoscopic endonasal approach , Finite element analysis , Mechanical measurements , Pituitary surgery , Reverse engineering

Recent technological advances in the field of Additive Manufacturing (AM) and the increasing need in Regenerative Medicine (RM) for devices that better and better mimic native tissue architecture are showing limitations in the current scaffolds fabrication techniques. A switch from the typical layer-by-layer approach is needed to achieve precise control on fibres orientation and pores dimension and morphology. In this work a new AM apparatus, the RAVEN (Robot-Assisted Volumetric ExtrusioN) system, is presented. RAVEN is based on a 7-DOF robotic arm and an extruder and allows for volumetric extrusion of polymeric filaments. The development process, namely the robotic motion optimisation, the optimisation towards small-scale trajectories, the custom-made hardware/software interfaces, and the different printing capabilities are hereby presented. The successful results are promising towards future advanced applications in which the ability of the robot to change its configuration while printing will be crucial.

Keywords: Design for additive manufacturing , measurements , regenerative medicine , robot-assisted volumetric extrusion , robotic arm , small-scale manufacturing

This study aims to evaluate the functionality and effectiveness of a new tool for ironing 3D printed components using FDM technology. The ironing process draws inspiration from the ball burnishing process, employing a spherical tool that, by exerting pressure, modifies the component's surface. The process involves heat transfer between the tool and the component to soften and deform the surface more easily. A theoretical analysis of the resulting surface roughness following this process was conducted to identify the parameters at play and their influence. It was found that the two main parameters are the fillet radius and the distance between passes. Additionally, practical tests were carried out to confirm the hypotheses, revealing the influence of three other parameters: tool Z-axis positioning, temperature, and movement speed. The surface after treatment exhibits an average roughness of Ra = 0.378 μm, significantly better than values obtained with ironing performed with the nozzle and with quality comparable to grinding. This lays the groundwork for possible future optimization of parameters based on the component's characteristics and the material used.

Keywords: additive manufacturing , burnishing , FDM , Ironing , surface treatment

Extrusion-based 3D printing is widely used in various industries, enabling the rapid creation of prototypes and functional parts at a lower cost compared to traditional technologies. However, one limitation of this technology is the surface finish of the components, where the roughness is not optimal. This paper presents a solution to improve the surface finish of components by leveraging the principles of ironing and ball burnishing. The tool, appropriately heated and passed over the component, smooths out imperfections by uniforming the surface profile. Unlike the traditional ironing method, the use of this tool reduces processing times and enhances surface roughness with a simpler construction and control mechanism compared to a conventional hot-end. The average surface roughness obtained is Ra = 0.796 μm; these values are comparable to those obtained by grinding. The geometry of the tool tips can vary significantly based on specific applications.

Keywords: additive manufacturing , burnishing , custom tool , enhancement of surface finish , Ironing , material extrusion

In this study, the crashworthiness behavior and energy absorption capacity of composite tubes under lateral indentation by steel rods aligned parallel to the specimen axis are investigated using experimental methods. Key parameters such as tube diameter, length, wall thickness, and indenter diameter are systematically examined and compared. Additionally, the influence of polyurethane foam fillers on damage modes and energy absorption capacity is rigorously analyzed. Contrary to conventional findings, smaller diameter specimens filled with foam demonstrate superior energy absorption compared to their larger counterparts, primarily due to enhanced compression of the foam volume. Experimental results reveal a complex interplay of damage mechanisms in composite specimens, including matrix cracking, fiber breakage, foam crushing, foam densification, foam fracture, and debonding of composite layers, all contributing to enhanced energy absorption. Increased tube thickness, length, and indenter diameter, alongside decreased tube diameter, are correlated with higher contact forces and improved energy absorption. Smoother shell fractures are promoted, and overall energy absorption capabilities are enhanced by the presence of foam fillers. This investigation provides valuable insights into the structural response and crashworthiness of composite tubes, which is essential for optimizing their design across various engineering applications.

Keywords: composite tube , energy absorption , lateral indentation , lightweight design , polyurethane foam

The growing demand for sustainable materials has significantly increased interest in biocomposites, which are made from renewable raw materials and have excellent mechanical properties. The use of machine learning (ML) can improve our understanding of their mechanical behavior while saving costs and time. In this study, the mechanical behavior of innovative biocomposite sandwich structures under quasi-static out-of-plane compression was investigated using ML algorithms to analyze the effects of geometric variations on load-bearing capacities. A comprehensive dataset of experimental mechanical tests focusing on compression loading was employed, evaluating three ML models—generalized regression neural networks (GRNN), extreme learning machine (ELM), and support vector regression (SVR). Performance indicators such as R-squared (R2), mean absolute error (MAE), and root mean square error (RMSE) were used to compare the models. It was shown that the GRNN model with an RMSE of 0.0301, an MAE of 0.0177, and R2 of 0.9999 in the training dataset, and an RMSE of 0.0874, MAE of 0.0489, and R2 of 0.9993 in the testing set had a higher predictive accuracy. In contrast, the ELM model showed moderate performance, while the SVR model had the lowest accuracy with RMSE, MAE, and R2 values of 0.5769, 0.3782, and 0.9700 for training, and RMSE, MAE, and R2 values of 0.5980, 0.3976 and 0.9695 for testing, suggesting that it has limited effectiveness in predicting the mechanical behavior of the biocomposite structures. The nonlinear load-displacement behavior, including critical peaks and fluctuations, was effectively captured by the GRNN model for both the training and test datasets. The progressive improvement in model performance from SVR to ELM to GRNN was illustrated, highlighting the increasing complexity and capability of machine learning models in capturing detailed nonlinear relationships. The superior performance and generalization ability of the GRNN model were confirmed by the Taylor diagram and Williams plot, with the majority of testing samples falling within the applicability domain, indicating strong generalization to new, unseen data. The results demonstrate the potential of using advanced ML models to accurately predict the mechanical behavior of biocomposites, enabling more efficient and cost-effective development and optimization processes in the field of sustainable materials.

Keywords: biocomposites , extreme learning machine , generalized regression neural networks , machine learning , support vector regression

This study examines the mechanical performance of bio- and bio-inspired composite sandwich panels under out-of-plane compressive loading. Using balsa wood face sheets and natural oak tree cupules for core reinforcement, the research aims to develop lightweight, sustainable structures. Three different cupule arrangements and geometries were tested, with and without polyurethane (PU) foam infill, to compare natural and commercial cores. The results show that cupule geometry significantly influences mechanical behaviour, demonstrating notable load-bearing and energy absorption capabilities, though with a tendency towards brittle fracture. Reinforcement techniques, such as cupule duplication and the integration of commercial PU foam, were explored to enhance load-bearing capacity and energy absorption while mitigating sudden failure. Panels reinforced with oak tree cupule-filled PU foam cores exhibited superior mechanical properties compared to non-reinforced cores. This study highlights the potential of using oak tree cupules to improve mechanical performance and sustainability in sandwich panel design. Furthermore, bio-inspired structures were fabricated via Multi Jet Fusion additive manufacturing, harnessing the unique properties of cupules to achieve superior mechanical performance in printed structures, emphasising their potential in structural engineering applications.

Keywords: additive manufacturing , Bio-composite , bio-inspired , lightweight design

Environmentally friendly sandwich structures made of composite materials are the key to sustainable innovation. They offer lightweight, durable solutions that reduce emissions in numerous industries. Machine learning (ML) streamlines the prediction of the mechanical behavior of materials, drastically reducing both the time and cost of material design and development. This study investigates the mechanical behavior of novel environmentally friendly composite sandwich structures under quasi-static out-of-plane compression loading using ML algorithms. The research focuses on assessing how the geometric dimensions affect the strength of these panels when used as load-bearing structures. Random forest (RF) and ridge regression methods were employed to assess the linearity and nonlinearity relationships within a database of 42,280 data points, focusing on the compression load as the target variable. The results show for the test data set that the RF model has a higher accuracy with an R-squared value of 0.9990, root mean square error of 0.1532 and a mean absolute error of 0.7030, which underlines the non-linearity of the relationship. Conversely, linear regression was found to be unable to effectively capture complexity, which is reflected in its lower performance. Furthermore, the uncertainty analysis shows that the RF model has a minimal bias and higher precision and clearly outperforms the ridge regression in terms of predictive accuracy and consistency. The analysis of the importance of the feature contribution reveals that the number of cupules has the greatest influence and the mechanical properties of the balsa wood have the least influence on the compressive strength of the composite panels. This study highlights the effectiveness of ML in predicting the mechanical behavior of environmentally friendly composite materials and structures, emphasizing the crucial role of geometric parameters and material properties in improving prediction accuracy and reliability.

Keywords: Bio-composite structures , Machine learning analysis , Mechanical property , Random Forest , Ridge regression

This study presents a novel approach to creating lightweight corrugated sandwich panels using additive manufacturing, inspired by eco-friendly green sandwiches composed of Oak-tree cupules as core elements and balsa face sheets. The bio-inspired structures were fabricated using the Multi Jet Fusion method, employing Nylon PA12 GB infused with 40% glass beads. Through experimental evaluation, the quasi-static compressive characteristics and energy absorption behavior of the bio-inspired sandwich panels were examined to analyze the impact of geometrical dimensions on their ability to withstand compressive loads and absorb energy. Results indicate that the printed panels outperformed the bio-based samples of identical dimensions, demonstrating approximately five times greater peak load and energy absorption. Additionally, the performance of the printed panels regarding load capacity and energy absorption was found to be influenced by their geometrical dimensions.

Keywords: Additive manufacturing , Bio-inspired , Composite , Mechanical property , sandwich structure

In this study, the optimal design of an Energy Storage System (ESS) for automotive application is proposed with particular focus on thermal, geometrical and efficiency parameters. In particular, the evaluations are referred to the case study of a real urban electric vehicle, equipped with a Li-NMC battery pack and a forced air-based Battery Thermal Management System. This study starts from modelling activities, concerning the development of the ESS and vehicle propulsion system models using a multi-disciplinary approach. Then, a battery pack CAD model is developed for the evaluation of the space bulk inside the vehicle chassis. Exploiting these models, the battery pack design is obtained through a multi-objective optimization strategy, to minimize cells' temperature and power, required for cooling systems under different constraints. The procedures determine the best layout solution in terms of battery cells distance and cooling fan mass flow rate value in order to reach the optimization goals, mainly in terms of significant reductions in battery pack size and cooling power, while maintaining equal thermal performance.

Keywords: Battery Electric Vehicles , Battery Thermal Management System , Multi-Domain Modelling , Multi-Objective Optimization

The human skeleton is the frame of the human body and is made up of bones of different shapes and characteristics. Some of the bones in the human body must have high resistance to static and dynamic forces. These forces are also exerted on bone replacement implants, which must have a high level of robustness. Some of the most important properties of bone material, although different in various regions of the body, are tensile and compressive strength. These properties could be tuned for prosthetic applications by using lattice structures that can be varied in shape, unit cell size, and strut thickness. One technique that could be used to fabricate such structures is selective laser melting (SLM), which builds a three-dimensional object layer by layer from a metal powder. Another major advantage of this technology is the ability to create many different shapes, as there is a high degree of design freedom. In addition, various metal powders can be used in SLM, including those that could be used for implants such as titanium. Another major advantage of this manufacturing process is the ability to easily customize the process to the size, shape, and other characteristics of the patient's bone structure. This work demonstrates the potential to adjust the tensile properties of objects with specific external dimensions by incorporating lattice structures and tailor them. This can impact both the ultimate tensile strength and maximum elongation, providing an opportunity for future research to more accurately replicate the properties of bone material and thereby establish an additively manufacturable bone replica for stress analysis in medical and sports research. Additionally, the surface roughness Ra, which measures approximately 10 to 15 µm, did not appear to have any significant effect on the tensile strength.

Keywords: Lattice structures , Medical Prosthetics , Selective Laser Melting

This paper examines the capabilities of injection moulding in the production of medical devices, with a particular focus on the potential of additive manufacturing in the construction of cooling channels. Conventionally, cooling channels in injection moulds have been limited to linear configurations. However, the size, integration and structure of the cooling channels have a massive impact on cooling efficiency, part quality, cycle time and overall process efficiency. This leads to the need for new structural manufacturing methods. The introduction of additive manufacturing offers freedom of design and the ability to create cooling channels that conform to the tool's geometry. With the new advantages introduced by additive manufacturing come new difficulties and innovations in the development of conformal cooling channels. Examined through a case study of a medical component in this paper. Producing cooling channels that conform to the complex contours of the mould requires complex design processes. This paper investigates the challenges of designing these channels to ensure precision and efficiency in the development of conformal cooling channels. A key focus is the comparative analysis between optimised cooling channels, crafted through additive manufacturing, and their conventionally machined counterparts. Using simulation techniques, the study examines the reduced temperatures achieved by the conformal channels compared to their conventional counterparts. The implications of integrating additive manufacturing into the injection moulding tool for medical devices are also discussed. By incorporating additive manufacturing into the process of creating the cooling channels, increased efficiency and reduced temperature are achieved, as demonstrated by the simulation results. Within the scope of this study, emphasis is placed on investigating the potential impact on part quality, manufacturing speed, and overall process efficiency, beyond the immediate benefits observed in cooling channel design.

Keywords: Additive Manufacturing , Conformal Cooling Channels , Metall-3D-Prinitng

In modern society, it is increasingly important to pay attention to healthy and aesthetic appearance. Orthodontic treatment can improve the position of teeth and jaw. A distinction was made between functional orthodontic and dentoalveolar therapies. Currently, various materials are used to produce brackets. In addition to conventional metal brackets made of steel, titanium, or gold, tooth-colored plastic brackets made of polycarbonate and ceramic brackets made of aluminum or zirconium oxide are used. [1, 2] The progressive development of CAD/CAM technology in recent years has opened new possibilities for computer-assisted orthodontic diagnostics and planning for dentoalveolar therapy. [3, 4] The aim of this study was to manufacture plastic brackets for fixed braces using simple additive production. For this purpose, a bracket sample was designed and produced with a filament printer (FDM-Fused Deposition Modelling) Ultimaker S3 (Ultimaker B.V., Watermolenweg 24191 PN Gel-dermalsen). The construction and design were created using CAD software (SolidWorks2021) and transferred to 3D printer's proprietary slicing software (Cura Version 4.12.0, Ultimaking ltd.). By adjusting the parameters (printing speed, temperature, etc.), it is possible to adapt the printed components. The printed miniature components were then optically and physically tested and analyzed.

Keywords: 3 D Printing , dentistry , fixed orthodontic treatment , synthetic brackets

This work presents the development, prototyping and validation of a new concept of structure called structural smart fabric (SSF). An SSF is a chainmail fabric composed by interconnected elements, called cells, which can be stiffened by reactive elements, such as those made by shape memory alloy (SMA). Exploiting the shape memory functionality, SSFs are capable of sensing and reacting to external stimuli. Based on this concept, in this study we propose an SSF that integrates SMA wires into a 3D-printed chainmail structure. The shape memory effect of these wires is used as an actuating mechanism that, at high temperature, tightens the adjacent cells together and provides increased structural stiffness. In this study, finite element simulations were initially conducted to enhance the comprehension of the SSF's mechanical behaviour. The influence of the initial cell spacing on the SSF stiffness and the wire stress profiles was evaluated during bending loading, as well as the evolution of contact pressure profiles between adjacent cells. This numerical approach enabled to tune the design of the SSF prototypes which were successively manufactured using selective laser sintering additive manufacturing technology with PA12. After the integration with the SMA wires, the SSF prototypes were tested under a 3-point bending configuration at different temperatures. The results revealed a remarkable increase in structural stiffness at elevated temperatures compared to ambient conditions. This study set the basis for a deeper understanding of SSF's unique capabilities and potential applications in fields where adaptive and responsive structures are required.

Keywords: 3D printing , chainmail , fabrics , SMA , smart structures , variable stiffness

In this study, an analysis was conducted to quantify errors in the additive manufacturing process, with a focus on comparing data files. The primary objective was to minimise the reliance on physical testing of produced components by favouring verification within a virtual environment. The initial focus was on the conversion from the CAD (Computer-Aided Design) model to the STL (Standard Tessellation Language) file, where discrepancies between the two formats were identified. To achieve optimal meshing, it is crucial to configure conversion parameters effectively, avoiding both detail loss and the handling of excessively large files. Following this, a comparison between STL files and reconstructed G-code files was conducted, uncovering further approximations introduced by the most commonly used slicers. In conclusion, the analysis highlights that both the quality of the mesh and the slicing phase significantly impact the final component. Understanding these factors is essential for achieving an optimal print outcome.

Keywords: Dimension accuracy , G-code , Material extrusion , Model conversion , STL

Soft Pneumatic Actuators (SPAs) have a simple structure of inner chambers that can be deformed to generate force when pressurized. Additive Manufacturing (AM) has been used to enhance SPAs by leveraging design freedom and customization for specific tasks. This study aims to develop a Finite Element Method (FEM) model to simulate a generic 3D-printed bellow SPA performing planar bending motion under pressure. The model, which can be used with different SPA design parameters, evaluates properties such as bending angle and exerted forces. The performance of a bellow SPA is influenced by factors such as its main dimensions, material, operating pressure, and chamber shape. This study considered three main contributors to bending behavior: wall chamber thickness, the number of bellow segments, and operating pressure. Various configurations were 3D-printed in Thermoplastic Polyurethane (TPU) and tested using a physical rig. FEM analyses were conducted within Ansys 2019 using static structural analysis. The second-order Ogden model was chosen for the hyper-elastic material according to reference [1]. Material parameters were identified for limited configurations through an optimization process that minimized the bending angle error. Other actuator configurations were simulated using various sets of parameters and compared with physical test results. The results showed a valid approximation across different actuator configurations. This study has developed an effective methodology to simulate the behavior of a generic 3D-printed TPU bellow SPA with a satisfactory level of approximation. © 2024, Scipedia S.L.

Keywords: 3D printing , Modeling , Pneumatic Actuator , Soft Actuator , Soft Material

Autologous ear reconstruction is a surgical procedure aimed at total or partial reconstruction of the ear of patients with microtia, which is a malformation or absence of the anatomical element. This procedure involves harvesting cartilage material from the patient at the costal level, cut and sculpt the tissue to create the skeleton of the ear, which is then inserted into a subcutaneous pocket in the malformed region. The sculping procedure takes inspiration from the geometry of the patient's contralateral healthy ear and can be very complex given the particularity of the elements to be reconstructed. Taking advantage of Reverse Engineering and Additive Manufacturing technologies, the authors, in collaboration with hospital staff, proposed patient-specific three-dimensional cutting guides that can facilitate the surgeon in sculpting the ear structure. In this work, a fully automated procedure for creating the printable digital model of such guides is proposed: at first is performed the segmentation of ear elements, which represent the starting data for the automated CAD modelling of the surgical guides. The automatic CAD modelling procedure was tested and validated by medical personnel.

Keywords: CAD Modelling , Ear Reconstruction , Reverse Engineering , Surgical Guide