Additive manufacturing

Stroke survivors often face significant upper limb impairments, necessitating frequent therapy and rehabilitation to regain function. However, frequent visits to medical centers for treatment sessions pose challenges to the patient. We present GripDome V2, an enhanced prototype for customizable grip strength measurement and rehabilitation. It enables unsupervised home use, improving accessibility. The device features an inflatable silicone dome connected to a PLA base, with grip strength measured via a pressure transducer and microcontroller, by which the patient's progress can be measured and quantified. The prototype offers adjustable resistance levels, facilitating personalized rehabilitation progress tracking. 3D printing is used for plastic components and silicone molds, enhancing affordability and customization. Experimental tests demonstrate GripDome's ability to provide consistent and reliable measurements even under prolonged use. The pneumatic system allows dynamic adjustment of resistance levels, tailoring rehabilitation paths to individual needs. Additionally, a basic game-like exercise has been integrated to motivate users, making the rehabilitation process more engaging and enjoyable. The device shows promise for reliable quantitative grip strength assessment and at-home rehabilitation for stroke survivors.

Keywords: 3D printing, additive manufacturing, rehabilitation , Hand rehabilitation , Personalized rehabilitation , Pneumatic , Rehabilitation devices , Silicone , Soft actuator

This paper presents a novel workflow for designing and fabricating patient-specific cranial implants and surgical guides using advanced CAD and 3D printing technologies. Developed at the T3Ddy Lab at Meyer Children's Hospital, the methodology includes close collaboration between engineers and neurosurgeons, allowing for rapid prototyping and real-time adjustments. The workflow involves designing a cutting guide for precise bone resection and a custom cranial implant for anatomical reconstruction, both using titanium alloy for its biocompatibility and mechanical strength. The setup enhances efficiency, ensuring precise device fit and improving surgical outcomes. The paper presents the general framework established to design custom made medical devices for skull repairing surgery. The CAD modeling phase of the surgical guide and cranial implant are described in detail, highlighting developed solutions to improve the effectiveness of the technology. The proposed workflow was validated in multiple clinical cases, demonstrating improved surgical precision, reduced operative time, and enhanced recovery.

Keywords: 3D Printing , CAD , Cranial Implant , Cranioplasty , Craniotomy , Surgical Guide

This study investigates the impact of Voronoi tessellation parameters on the dynamic behaviour of a leaf spring employed in a tensioner for internal combustion engine timing systems. The research utilizes a custom-developed algorithm to apply Voronoi tessellation while maintaining a constant mass by varying the number of seeds and scaling factors. Modal simulations were carried out to determine the natural frequencies, participation factors, and mode shapes of the component. These simulations were validated through experimental tests performed using a hammer impact test, which provided reliable and detailed measurements of the system's dynamic response under different loading conditions. The experimental results revealed a strong correlation with the simulated data, demonstrating the accuracy of the proposed methodology. By modifying Voronoi tessellation parameters, it was possible to control and predict changes in natural frequencies and participation factors. These findings underscore the potential of Voronoi tessellation in dynamic system optimisation, enabling the design of components with tailored properties for improved performance. This research opens new avenues for integrating Voronoi-based design approaches with topology optimisation, particularly in the frequency domain, paving the way for innovative applications in mechanical and structural engineering.

Keywords: CAE approach , Modal frequencies , Optimal mass distribution , Participation factors , Tensioner leaf spring , Topology optimisation , Word

Intraosseous access is a critical emergency procedure for introducing fluids and blood components into the systemic circulation, necessitating thorough training for healthcare personnel. In collaboration with Meyer Children's University Hospital IRCCS, the Department of Industrial Engineering at the University of Florence has developed a high-fidelity, low-cost pediatric simulator specifically for intraosseous access in the proximal tibia. Using Materialise Mimics 26® software, anonymized tomographic images of a 5-year-old patient were processed to obtain the three-dimensional geometry of relevant anatomical structures: tibia, fibula, distal femur, muscles, and skin. These models were further refined with Geomagic DesignX® for the final simulator design. The simulator features silicone replicas for soft tissues, with rigid structures and silicone casting molds produced through Fused Deposition Modeling. Preliminary tests were conducted to select suitable materials, which were then evaluated by specialized medical personnel using the EZ-IO® device to ensure accurate haptic feedback. The resulting simulator provides a realistic platform for intraosseous access training, enabling healthcare practitioners to enhance their procedural skills and ensure patient safety.

Keywords: additive manufacturing , intraosseous access , pediatric mannequin

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. © 2025 The Author(s).

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

Additive manufacturing is one of the foundational pillars of Industry 4.0, which is rooted in the integration of intelligent digital technologies, manufacturing, and industrial processes. Machine learning techniques are resources used to support Design for Additive Manufacturing, particularly in design phases and process analysis. Neural Networks are suited to manage complex and non-linear datasets. The article proposes a methodology for the time and cost assessment of the Laser-Powder Bed Fusion 3D printing process using a Neural Network-based approach. The methodology analyzes the main geometrical features of STL files to train Neural Network Machine Learning models. The methodology has been tested on a preliminary dataset that includes a set of parametric CAD models and their corresponding Additive Manufacturing simulations. The trained models achieve an R2 value greater than 0.97. A web-service platform has been implemented to provide a valuable tool for users, transforming a research-grade model into a production-grade online endpoint.

Keywords: 3D printing cost , design for additive manufacturing , laser powder bed fusion , machine learning , neural networks

Additive manufacturing processes could reduce environmental impacts by optimizing the material distribution and achieving lightweight products. The sustainability of the additive processes can be further improved by using recycling methods. The most used practices are reusing metal powders for multiple builds and recycling components throughout the additive manufacturing process to make recycled powders. This paper addresses the current state of the art recycling practices regarding the use of metal powders in Laser-Powder Bed Fusion process propose an approach to consider the environmental impact of the product during the design phase. The approach considers a mix of virgin, reused, and recycled powders as the material input. The design optimization workflow and Life Cycle Assessment provide useful tools to achieve feasible and sustainable solutions.

Keywords: DfAM , L-PBF , Laser-Powder Bed Fusion , Recycled powders

The machine learning usage in the L-PBF process for metal powders helps to identify the optimal parameter combination. Machine learning can find non-linear correlations between the high number of variables of this production process. One of the obstacles to the widespread adoption of L-PBF in the industry, in addition to the high costs, is the long printing time required for a complex component. The possibility of an early evaluation of the 3D printing time could promote the overall diffusion of this production process in the industry. Correct time prediction can improve cost efficiency and production capacity, reducing energy consumption, environmental impacts, and lead time. This paper proposes a machine learning approach, such as Random Forest Regressor, to predict the printing time of a metal component starting from the STereo Lithography (.stl) CAD format for the L-PBF process. A case study is proposed to evaluate and demonstrate the approach, obtaining a high-level prediction accuracy.

Keywords: Design for Additive Manufacturing , L-PBF Process , Machine Learning , Printing Time

This work aims at improving the quality, meaning both dimensional accuracy and green density, of binder jetting products by the optimization of printing parameters. Cubes with a squared through hole were printed using AISI 316L stainless steel powder, varying four printing parameters: dark body (binder saturation), shell thickness, printhead, and powder spreading speed. Taguchi methodology was employed to design nine orthogonal experiments, permutating the four factors at three levels. The green density was measured by mass to volume ratio, and the dimensions of the printed parts were derived by coordinate measuring machine (CMM) measurements. Green density and dimensional accuracy were the response variables to be optimized. It was observed that green density varied in the interval (0.53 ÷ 0.57) of relative density; ANOVA (ANalysis Of VAriance) showed that the highest contribution to maximize green density (81%) is given by dark body. Concerning dimensional accuracy, the difference between measured and nominal dimensions, referred to nominal dimensions, varied between 0.53% and −1.01%. Dimensions parallel to binder injection direction (X) are mostly affected by shell thickness and dark body (35% and 55%, respectively). Printhead speed and powder application speed, instead, mostly affect dimensions parallel to powder spreading direction (Y) and dimensions aligned with building direction (Z)—printhead speed 56% for Y and 37% for Z, powder application speed 30% for Y and 24% for Z, respectively. A physical explanation is discussed, moreover linear regression models are proposed for optimizing parameters.

Keywords: Additive manufacturing , Design of experiment , Dimensional accuracy

Metal Binder Jetting (MBJ) has a great potential for producing complex products in medium to large batches. However, challenges remain in achieving high dimensional and geometrical accuracy. This study investigates the influence of printing parameters and sample geometry on the accuracy of 316L stainless steel green products produced via MBJ. A Design of Experiments (DoE) approach was employed to study four factors at three levels. A Taguchi L9 orthogonal array was designed to evaluate the effects of printhead speed, binder saturation grade, blade speed, and shell thickness on dimensional and geometrical accuracy. Four different geometries were printed and then measured using a coordinate measuring machine (CMM) to assess the accuracy of external and internal dimensions, flatness, and parallelism. The results showed that dimensional accuracy was influenced by printing direction, feature size, and the feature type. External dimensions showed highest accuracy along the powder spreading direction (Y: 0.12% ± 0.21%), followed by building direction (Z: 0.25% ± 0.3%) and binder injection direction (X: 0.40% ± 0.2%). Significantly lower accuracy was found in internal features, showing a negative deviation (X: − 0.66% ± 0.32%; Y: − 0.41% ± 0.23%). Flatness and parallelism errors were found almost constant across experiments, with average values of 0.014 ± 0.008 and 0.024 ± 0.014, respectively. Parallelism appeared to be affected by geometry, specifically, increasing height-to-thickness ratios led to larger errors. Grey relational analysis identified printhead speed as the most critical parameter, followed by binder saturation grade, shell thickness, and blade speed in optimizing the accuracy of the printed parts. ANOVA analysis reveals that printhead has a statistical relevance showing a P-value of 0.029. This work provides novel insights into the printing factors affecting the quality of green parts produced by MBJ. This article helps in identifying the optimal printing parameters for binder jetting of SS 316L.

Keywords: Design for additive manufacturing , Dimensional accuracy , Geometrical accuracy , Grey relational analysis , Metal binder jetting

This study investigates the accuracy of microchannels fabricated using binder jetting. The research aims to contribute to the future development of continuous-feeding anodes for direct carbon fuel cells (DCFC) within the framework of the PRIN PNRR JETCELL project. The anodes are engineered with tailored microchannel architectures to maximize surface area to improve the electrochemical efficiency, facilitate the electrolyte flow, and prevent the fuel leakage. Stainless steel 316L and red mud composites were employed, as stainless steel provides high mechanical properties, while red mud enhances sustainability and functionality for anode applications. By systematically varying microchannel dimensions and spacings, the manufacturability and structural fidelity of micro-scale features critical to device performance have been assessed. Dimensional analysis, conducted via confocal microscopy, reveals key insights into shape retention and dimensional accuracy. To address sintering-induced shrinkage and dimensional deviation induced by printing process, scaling factors law as a function of nominal channel size have been proposed, paving the way for more predictable and precise fabrication of advanced functional components. © N. Shang et al.

Keywords: binder jetting , composite materials , dimensional accuracy , direct carbon fuel cell , Microchannel architectures , red mud

Directed Energy Deposition is mainly adopted for the construction of large parts and structures or the reparation of worn and damaged components, but also in Hybrid Manufacturing systems, to combine features of different technologies. An additional emerging application is the Remanufacturing of existing components by integrating further features and materials to enhance products' functionality. Nevertheless, residual stress and strain are among the most relevant drawbacks since thermal gradients and cooling rates are more intensive compared to other Metal Additive Manufacturing processes. With the aim to define the build strategies (e.g.: tool paths and scanning patterns) for multiple depositions on actual existing components and mitigate the substrate deformation, this work is focused on the laser-based Direct Metal Deposition process design and optimization. To find the best trade-off between process efficiency and product quality, the goal is achieved identifying the build strategies (i) to guarantee the expected product requirements and (ii) to provide functional and assembly requirements. The case study is the Additive Remanufacturing of an automotive component, requiring a functional tolerance control, which implies to contain process-induced distortion. The build process is assessed through Finite Element thermo-mechanical simulations of 316L deposition, cooling, and unclamping steps. The effects of factors as the deposition strategy, orientation, direction, and sequence, are studied through a Design of Experiments approach. The model maximum displacement is the primary response, and the build time is the secondary one.

Keywords: AISI 316L , Finite element method , Laser metal deposition , Process optimization , Process simulation

The CuCrZr alloy has garnered significant interest as a promising material for additive manufacturing, particularly in applications requiring high strain rate performance. Such applications include vertical targets like heat sinks in the ITER divertor and actively cooled plasma-facing components, where these alloys serve as structural materials. Although the dynamic behaviour of additively manufactured materials is an expanding area of study, the high strain rate properties of CuCrZr remain not exhaustive and require further investigation. This study presents the results of quasi-static and dynamic tension–compression tests conducted at various strain rates on CuCrZr alloy specimens in their as-built condition. The alloy was fabricated using laser powder bed fusion (L-PBF) with selective laser melting (SLM) technology. Compression samples were designed with standard cylindrical shapes, whereas tensile sample geometry was tailored to meet dynamic testing requirements. The study involves the calibration of an improved Johnson–Cook constitutive model through inverse analytical and numerical procedures. Dynamic increase factors (DIFs) were also evaluated using phenomenological and physical model parameters. The findings indicate that CuCrZr alloy exhibits strain rate sensitivity, which activates above a certain threshold. This was confirmed by a reconstructed dynamic fracture locus, which was consistently higher than the quasi-static locus across all stress triaxiality values.

Keywords: CuCrZr alloy , DIF , high strain rate , SLM , triaxiality

This paper investigates the mechanical behaviour of biomimetic structures produced using L-PBF technology. Specifically, the triply periodic minimal surface (TPMS) gyroid structure was analysed. The TPMS are receiving significant attention in biomedical applications to produce structures with high strength-to-weight ratio, better control over the transition in properties between the prosthesis and the bone tissue, such as stiffness and density, and improved interface adhesion. The mechanical characterisation of these structures is the current challenge for researchers. We propose a preliminary modelling strategy for a homogenisation procedure based on the Representative Volume Element (RVE) theory and traditional mesoscale and macroscale finite element (FE) analysis integrated with evaluating the anisotropy induced by the printing process. The results indicate that the L-PBF produces an orthotropic base material, the unit cell exhibits an anisotropic behaviour, and the macroscopic modelling reduces the computational cost. However, a mesoscopic approach is necessary to define the anisotropic elastic matrix of the cell. © 2025, Association of American Publishers.

Keywords: Anisotropy , Biomimetic , L-PBF , Meso and Macroscale , TPMS

Life cycle engineering is a crucial design methodology to support optimized design choices in the context of ecodesign, particularly for components manufactured with novel technologies such as additive manufacturing. This study presents a systematic literature review on the topic, analysing 87 publications following the PRISMA framework. The review explores the environmental and economic life cycle implications of additive manufacturing in relation to the technological features and engineering design aspects. Additionally, it shows existing frameworks for integrating ecodesign into sustainability assessments of additive manufacturing and the development of ecodesign guidelines. From an engineering perspective, these technologies introduce several advantages enabling the creation of lightweight, multi-functional, and performance-optimized components. However, challenges related to high energy consumption, material preparation, and variability in life cycle impacts need to be addressed with dedicated life cycle analysis to ensure environmental and economic sustainability. Existing methods for integrating ecodesign into life cycle assessment are fragmented, with limited frameworks combining environmental and economic aspects. Emerging approaches, such as automated life cycle assessment tools, show promise but remain underexplored. Ecodesign guidelines emphasize well-known strategies such as material optimization and waste reduction. Nonetheless, translating these guidelines into standardized practices remains a challenge.

Keywords: 3D printing , Design for Sustainability , Ecodesign , LCA , LCC , Life Cycle Assessment , Life Cycle Costing

Additive manufacturing (AM) is currently considered one of the most promising technologies for fostering innovation in the industrial sector, specifically in product design. Considering the non-subtractive nature of AM processes, in first approximation, they all seem to be environmentally sustainable since they lead to a null or at least reduced quantity of scraps. However, the peculiarities of such technologies, such as the specific input materials (e.g., metal powders, polymer filaments) or the low productivity, make the situation more complex, not so homogeneous in all the cases, and strongly dependent on the process and product parameters. In this complex scenario, the present paper aims to investigate the current state of the art regarding adopting Artificial Intelligence (AI) and Machine Learning (ML) toward the eco-design for AM. Following the PRISMA methodology, a set of 19 scientific papers were analysed. Papers were grouped according to the benefits (improvement of quality and reduction of energy, waste and defects) and design opportunities (definition of shapes and selection of materials) that AI and ML potentially provide. The discussion of the results presents the most relevant research and development opportunities for scholars and companies.

Keywords: Additive manufacturing , Artificial intelligence , Eco-design , Literature review , Sustainable design

The performance of parts produced using fused filament fabrication, a widely adopted additive manufacturing technology, is significantly influenced by build orientation. While previous studies have explored the effects of part orientation on mechanical and geometrical properties individually, their simultaneous interactions remain underexplored. This study addresses this gap by investigating the combined impact of build orientation, defined by three angles (α,β,γ), on tensile strength, flatness deviation, and surface roughness of PET-G (Polyethylene Terephthalate Glycol) specimens. The methodology consists of two stages: (i) an experimental campaign to establish the relationships between orientation angles and the output variables, and (ii) a surrogate-based optimization approach, employing the weighted-sum method and genetic algorithm to identify configurations that achieve optimal compromises between mechanical and geometrical properties. Results demonstrate a strong dependency of all outputs on the orientation angles, particularly the out-of-plane rotation, and a negative correlation between tensile strength and geometrical quality metrics. Optimal performance is confined to specific regions of the input space, emphasizing the importance of a precise orientation control. A graphical approach, inspired by Voronoi-like regions, illustrates the interdependencies among outputs and maps achievable compromises in the output space. These optimal zones correspond to limited regions in the input space. Moving away from these configurations results in a rapid decline in performance, underscoring the sensitivity of the process to build orientation and the need for tightly defined parameters to ensure high-quality parts.

Keywords: Build orientation , Flatness , Fused Filament Fabrication , Roughness , Surrogate-based optimization , Tensile strength

The geometrical performance of Additive Manufactured (AM) parts, particularly the influence of printing orientation on geometrical properties, is an open topic of investigation. The task of understanding geometry deviations across different printing orientations can be laborious, involving a multitude of experiments and the measurement of deviation fields. This study introduces a three-stage comprehensive methodology to address this issue. (i) An experimental investigation is conducted at varying printing orientation and the printed parts are captured by laser scanning; (ii) an automatic 3-2-1 alignment procedure of the scanned geometries is implemented, accurately replicating real positioning procedures; and (iii), Reduced-Order Modelling (ROM) is applied for the parametric mapping of deviation fields to orientation angles, creating a surrogate model of the non-nominal parts. Demonstrated with PET-G specimens, this approach can be applied to various geometries. Thanks to the surrogate model, the deviation field for any orientation can be obtained in real-time. As a result, the efficiency and accuracy of the AM process can be improved by reducing setup and scanning time when investigating different printing configurations. This allows manufacturers to focus more on part design and optimization, potentially leading to significant advancements in AM.

Keywords: 3-2-1 alignment , Additive manufacturing , Geometric deviations , Printing orientation , Reduced-order modelling

Background: Nonconventional fabrication technologies (e.g., additive manufacturing and 3D bioprinting) represent a challenging approach to the design of 3D scaffolds as extracellular matrix analogues with appropriate properties for supporting cell behavior over time. Methods: A strategy to develop 3D additive manufactured hybrid scaffolds with dual porosity and tailored morphological and mechanical/functional features was proposed via the combination of synthetic (poly-ε-caprolactone) and natural (chitosan) polymers. Design of 3D additive manufactured hybrid scaffolds, morphological analysis, in vitro swelling and degradation measurements, mechanical measurements, antimicrobial assays against both oral cavity-specific and nonoral bacteria, and biological assays using periodontal ligament stem cells (PDLSCs) or human osteosarcoma cells (MG63) were carried out. Results: The inclusion of the chitosan network improves the dimensional stability of the structure as well as the cell retention effect, ensuring antimicrobial activity.

Keywords: antimicrobial activity , dental, oral, and craniofacial tissue repair , design for additive manufacturing , hybrid structure , mechanical measurements , morphological analyses

Tuning materials' micro/nanoporous architecture is pivotal in maintaining microenvironmental homeostasis. Bioinspired micro/nanoporous scaffolds, based on triply periodic minimal surface (TPMS), present promising avenues for restoring microenvironmental equilibrium in tissue regeneration. Moreover, micro/nanoporous TPMS holds potential for applications in wastewater treatment and pollutant removal to restore microenvironmental homeostasis. 3D printing provides high flexibility in fabricating these multifunctional scaffolds with precise pore geometry and permeability control. However, printing micro/nanoscale pores using material extrusion (MEX) is still challenging. To address this issue, this study presents a novel approach to print soft micro/nanoporous scaffolds. A polymer blend of thermoplastic polyurethane elastomer (TPE) and polyvinyl alcohol (PVA) filament is selected to print gyroid-based scaffolds. Then, the water-soluble PVA molecules are removed, allowing for the creation of scaffolds with controllable porosity at the nanometer scales. The link between non-porosity, nanoporosity, mechanical behavior, and structure topology is investigated, and its impact on cell viability evaluated. Findings demonstrate that these 3D-printed scaffolds with multiscale porosity exhibit exceptional pollutant adsorption capabilities and enhance oil/water separation efficiency.

Keywords: Lattice structures , Material extrusion , Nanoporous structures , Superoleophilic , Superwetting , Triply periodic minimal surfaces