Search Results (27)

This study investigates the application of additive manufacturing (AM) in fabricating transverse thermoelectric (TTE) composites, demonstrating the feasibility of this methodology for TTE material synthesis. Zinc oxide (ZnO), a wide-bandgap semiconductor with moderate thermoelectric performance, and copper (Cu), a highly conductive metal, were selected as base materials. These were formulated into stable paste-like feedstocks for direct ink writing (DIW). A custom dual-nozzle 3D printer was developed to precisely deposit these materials in pre-designed architectures. The resulting structures exhibited measurable transverse Seebeck effects. Unlike prior TE research primarily focused on longitudinal configurations, this work demonstrates a novel AM-enabled strategy that integrates directional compositional anisotropy, embedded metal–semiconductor interfaces, and scalable multi-material printing to realize TTE behavior. The approach offers a cost-effective and programmable pathway toward next-generation energy harvesting and thermal management systems. Full article

In recent years, there has been a surge in the extrusion-based 3D printing of materials for various biomedical applications. This work presents a novel methodology for optimizing extrusion-based 3D bioprinting of a gelatin/siloxane hybrid material for biomedical applications. A systematic approach integrating rheological characterization, computational fluid dynamics simulation (CFD), and machine-learning-based image analysis, was employed. Rheological tests revealed a shear stress of 50 Pa, a maximum viscosity of 3 × 10⁵ Pa·s, a minimum viscosity of 0.089 Pa·s, and a shear rate of 15 rad/s (27G nozzle, 180 kPa pressure, 32 °C temperature, 30 mm/s velocity) for a BIO X bioprinter. While these parameters yielded constructs with 54.5% similarity to the CAD design, a multi-faceted optimization strategy was implemented to enhance fidelity, computational fluid dynamics simulations in SolidWorks, coupled with a custom-develop a binary classifier convolutional neuronal network for post-printing image analysis, facilitated targeted parameter refinement. Subsequent printing optimized parameters (25G nozzle, 170 kPa, 32 °C, 20 mm/s) achieved a significantly improved similarity of 92.35% CAD, demonstrating efficacy. The synergistic combination of simulation and machine learning ultimately enabled the fabrication of complex 3D constructs with a high fidelity of 94.13% CAD similarity, demonstrating the efficacy and potential of this integrated approach for advanced biofabrication. Full article

Metallic materials additive manufacturing is extremely challenging nowadays, while aircraft manufacturers are trying to adapt the newly developed technology to produce parts of complex geometry with minimum materials losses. Skyrora is a company focused on the production of several launch vehicles and rockets with the aim of becoming a commercial provider for access to space. One of the Skyrora goals is to develop innovative and long-term solutions for future growth, and, within the Horizon European project “MADE-3D”, aims to improve the rocket propulsion system of the launch vehicle Skyrora XL by exploiting multi-materials during the production phase by additive manufacturing. The main goal of the present investigation is to document the already existing production phases of the “conventional” Skyrora vacuum nozzle printed with Inconel 718 to provide a baseline in terms of weight, manufacturing cost, lead processing time and CO₂ equivalent emissions of the under-development multi-material demonstrator. Full article

Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions. Full article

Fused deposition modeling (FDM) 3D printing has the advantages of a simple molding principle, convenient operation, and low cost, making it suitable for the production and fabrication of complex structural parts. Moving forward to mass production using 3D printing, the major hurdle to overcome is the achievement of high dimensional stability and adequate mechanical properties. In particular, engineering plastics require precise dimensional accuracy. In this study, we overcame the issues of FDM 3D printing in terms of ternary material compounds for polyamides with gradient structures. Using multi-walled carbon nanotubes (MWCNTs) and boron nitride (BN) as fillers, polyamide 6 (PA6)-based 3D-printed parts with high dimensional stability were prepared using a single-nozzle, two-component composite fused deposition modeling (FDM) 3D printing technology to construct a gradient structure. The ternary composites were characterized via DSC and XRD to determine the optimal crystallinity. The warpage and shrinkage of the printed samples were measured to ensure the dimensional properties. The mechanical properties were analyzed to determine the influence of the gradient structures on the composites. The experimental results show that the warpage of pure polymer 3D-printed parts is as high as 72.64%, and the introduction of a gradient structure can reduce the warpage to 3.40% by offsetting the shrinkage internal stress between layers. In addition, the tensile strength of the gradient material reaches up to 42.91 MPa, and the increasing filler content improves the interlayer bonding of the composites, with the bending strength reaching up to 60.91 MPa and the interlayer shear strength reaching up to 10.23 MPa. Therefore, gradient structure design can be used to produce PA6 3D-printed composites with high dimensional stability without sacrificing the mechanical properties of PA6 composites. Full article

Material extrusion (MEX) additive manufacturing has successfully fabricated assembly-free structures composed of different materials processed in the same manufacturing cycle. Materials with different mechanical properties can be employed for the fabrication of bio-inspired structures (i.e., stiff materials connected to soft materials), which are appealing for many fields, such as bio-medical and soft robotics. In the present paper, process parameters and 3D printing strategies are presented to improve the interfacial adhesion between carbon fiber-reinforced nylon (CFPA) and thermoplastic polyurethane (TPU), which are extruded in the same manufacturing cycle using a multi-material MEX setup. To achieve our goal, a double cantilever beam (DCB) test was used to evaluate the mode I fracture toughness. The results show that the application of a heating gun (assembled near the nozzle) provides a statistically significant increase in mean fracture toughness energy from 12.3 kJ/m² to 33.4 kJ/m². The underlying mechanism driving this finding was further investigated by quantifying porosity at the multi-material interface using an X-ray computed tomography (CT) system, in addition to quantifying thermal history. The results show that using both bead ironing and the hot air gun during the printing process leads to a reduction of 24% in the average void volume fraction. The findings from the DCB test and X-ray CT analysis agree well with the polymer healing theory, in which an increased thermal history led to an increased fracture toughness at the multi-material interface. Moreover, this study considers the thermal history of each printed layer to correlate the measured debonding energy with results obtained using the reptation theory. Full article

The multi-scale study of rock properties is a necessary step in the planning of oil and gas reservoir developments. The amount of core samples available for research is usually limited, and some of the samples can be distracted. The investigation of core reconstruction possibilities is an important task. An approach to the real-size reconstruction of porous media with a given (target) porosity and permeability by controlling the parameters of FFF 3D printing using CT images of the original core is proposed. Real-size synthetic core specimens based on CT images were manufactured using FFF 3D printing. The possibility of reconstructing the reservoir properties of a sandstone core sample was proven. The results of gas porometry measurements showed that the porosity of specimens No.32 and No.46 was 13.5% and 12.8%, and the permeability was 442.3 mD and 337.8 mD, respectively. The porosity of the original core was 14% and permeability was 271 mD. It was found that changing the layer height and nozzle diameter, as well as the retract and restart distances, has a direct effect on the porosity and permeability of synthetic specimens. This study shows that porosity and permeability of synthetic specimens depend on the flow of the material and the percentage of overlap between the infill and the outer wall. Full article

3D printing of ceramics has started gaining traction in architecture over the past decades. However, many existing paste-based extrusion techniques have not yet been adapted or made feasible in ceramics. A notable example is coextrusion, a common approach to extruding multiple materials simultaneously when 3D-printing thermoplastics or concrete. In this study, coextrusion was utilized to enable multi-material 3D printing of ceramic elements, aiming to achieve functionally graded porosities at an architectural scale. The research presented in this paper was carried out in two consecutive phases: (1) The development of hardware components, such as distinct material mixtures and a dual extruder setup including a custom nozzle, along with software environments suitable for printing gradient materials. (2) Material experiments including material testing and the production of exemplary prototypes. Among the various potential applications discussed, the developed coextrusion method for clay-based composites was utilized to fabricate ceramic objects with varying material properties. This was achieved by introducing a combustible as a variable additive while printing, resulting in a gradient porosity in the object after firing. The research’s originality can be summarized as the development of clay-based material mixtures encompassing porosity agents for 3D printing, along with comprehensive material-specific printing parameter settings for various compositions, which collectively enable the successful creation of functionally graded architectural building elements. These studies are expected to broaden the scope of 3D-printed clay in architecture, as it allows for performance optimization in terms of structural performance, insulation, humidity regulation, water absorption and acoustics. Full article

A hydrogel system with the ability to control the delivery of multiple drugs has gained increasing interest for localized disease treatment and tissue engineering applications. In this study, a triple-drug-loaded model based on a core/shell fiber system (CFS) was fabricated through the co-axial 3D printing of hydrogel inks. A CFS with drug 1 loaded in the core, drug 2 in the shell part, and drug 3 in the hollow channel of the CFS was printed on a rotating collector using a co-axial nozzle. Doxorubicin (DOX), as the model drug, was selected to load in the core, with the shell and channel part of the CFS represented as drugs 1, 2, and 3, respectively. Drug 2 achieved the fastest release, while drug 3 showed the slowest release, which indicated that the three types of drugs printed on the CFS spatially can achieve sequential triple-drug release. Moreover, the release rate and sustained duration of each drug could be controlled by the unique core/shell helical structure, the concentration of alginate gels, the cross-linking density, the size and number of the open orifices in the fibers, and the CFS. Additionally, a near-infrared (NIR) laser or pH-responsive drug release could also be realized by introducing photo-thermal materials or a pH-sensitive polymer into this system. Finally, the drug-loaded system showed effective localized cancer therapy in vitro and in vivo. Therefore, this prepared CFS showed the potential application for disease treatment and tissue engineering by sequential- or stimulus-responsively releasing multi-drugs. Full article

The effect of printing parameters (nozzle diameter, layer height, nozzle temperature, and printing speed), dimensions (wall thickness), and filament material on the crashworthiness performance of 3D-printed thin-walled multi-cell structures (TWMCS) undergoing quasi-static compression is presented. The ideal combination of parameters was determined by employing the Signal-to-Noise ratio (S/N), while Analysis of Variance (ANOVA) was utilized to identify the significant parameters and assess their impact on crashworthiness performance. The findings indicated that the ideal parameters for the specific energy absorption (SEA) consisted of a nozzle diameter of 0.6 mm, layer height of 0.3 mm, nozzle temperature of 220 °C, printing speed of 90 mm/s, wall thickness of 1.6 mm, and PLA(+) filament material. Afterward, the optimal parameters for crushing force efficiency (CFE) included a nozzle diameter of 0.8 mm, layer height of 0.3 mm, nozzle temperature of 230 °C, print speed of 90 mm/s, wall thickness of 1.6 mm, and PLA(ST) filament material. The optimum parameter to minimize manufacturing time is 0.3 mm for layer height and 90 mm/s for printing speed. This research presents novel opportunities for optimizing lightweight structures with enhanced energy absorption capacities. These advancements hold the potential to elevate passenger safety and fortify transportation systems. By elucidating the fundamental factors governing the crashworthiness of thin-walled multi-cell PLA 3D-printed tubes, this study contributes to a deeper understanding of the field. Full article

Fused deposition modeling (FDM) technology is an emerging technology with promising applications, with the nozzle playing a crucial role in extrusion, heating, and material ejection. However, most current extrusion-based 3D printers handle only single-material printing, making the integration of multiple materials through a single nozzle challenging due to compromised quality and clogging risks. This paper introduces a method to design multi-material 3D printing nozzles using the Theory of Inventive Problem Solving (TRIZ) and knowledge graph (KG). By optimizing design and leveraging TRIZ’s contradiction resolution principle, this study addressed bottlenecks and complexities in multi-material nozzle design, providing insightful recommendations. A patent knowledge graph focused on spray nozzles was created, storing material properties, design elements, and constraints for enhanced knowledge sharing. Building on identified challenges and recommendations, the study utilized keyword searches and associative paths in the knowledge graph to guide designers in generating innovative solutions. Validation was achieved through two distinct nozzle design models resulting from guided innovations. The TRIZ-KG methodology presented in this paper provides designers with a systematic cognitive framework to empower designers in overcoming technical obstacles and proposing precise solutions. Full article

Allowing for complex shape and low energy consumption, 3D printing, debinding, and sintering (PDS) is a promising and cost-effective additive manufacturing (AM) technology. Moreover, PDS is particularly suitable for producing bimetallic parts using two metal/polymer composite filaments in the same nozzle, known as co-extrusion, or in different nozzles, in a setup called bi-extrusion. The paper describes a first attempt to produce bimetallic parts using Inconel 718 and AISI 316L stainless steel via PDS. The primary goal is to assess the metallurgical characteristics, part shrinkage, relative density, and the interdiffusion phenomenon occurring at the interface of the two alloys. A first set of experiments was conducted to investigate the effect of deposition patterns on the above-mentioned features while keeping the same binding and sintering heat treatment. Different sintering temperatures (1260 °C, 1300 °C, and 1350 °C) and holding times (4 h and 8 h) were then investigated to improve the density of the printed parts. Co-extruded parts showed a better dimensional stability against the variations induced by the binding and sintering heat treatment, compared to bi-extruded samples. In co-extruded parts, shrinkage depends on scanning strategy; moreover, the higher the temperature and holding time of the sintering heat treatment, the higher the density reached. The work expands the knowledge of PDS for metallic multi-materials, opening new possibilities for designing and utilizing functionally graded materials in optimized components. With the ability to create intricate geometries and lightweight structures, PDS enables energy savings across industries, such as the aerospace and automotive industries, by reducing component weight and enhancing fuel efficiency. Furthermore, PDS offers substantial advantages in terms of resource efficiency, waste reduction, and energy consumption compared to other metal AM technologies, thereby reducing environmental impact. Full article

Three-dimensional (3D) printing has various applications in many fields, such as soft electronics, robotic systems, biomedical implants, and the recycling of thermoplastic composite materials. Three-dimensional printing, which was only previously available for prototyping, is currently evolving into a technology that can be utilized by integrating various materials into customized structures in a single step. Owing to the aforementioned advantages, multi-functional 3D objects or multi-material-designed 3D patterns can be fabricated. In this study, we designed and fabricated 3D-printed expandable structural electronics in a substrateless auxetic pattern that can be adapted to multi-dimensional deformation. The printability and electrical conductivity of a stretchable conductor (Ag-RTV composite) were optimized by incorporating a lubricant. The Ag-RTV and RTV were printed in the form of conducting voxels and frame voxels through multi-nozzle printing and were arranged in a negative Poisson’s ratio pattern with a missing rib structure, to realize an expandable passive component. In addition, the expandable structural electronics were embedded in a soft actuator via one-step printing, confirming the possibility of fabricating stable interconnections in expanding deformation via a missing rib pattern. Full article

Developments in polymer 3D printing (3DP) technologies have expanded their scope beyond the rapid prototyping space into other high-value markets, including the consumer sector. Processes such as fused filament fabrication (FFF) are capable of quickly producing complex, low-cost components using a wide variety of material types, such as polylactic acid (PLA). However, FFF has seen limited scalability in functional part production partly due to the difficulty of process optimization with its complex parameter space, including material type, filament characteristics, printer conditions, and “slicer” software settings. Therefore, the aim of this study is to establish a multi-step process optimization methodology—from printer calibration to “slicer” setting adjustments to post-processing—to make FFF more accessible across material types, using PLA as a case study. The results showed filament-specific deviations in optimal print conditions, where part dimensions and tensile properties varied depending on the combination of nozzle temperature, print bed conditions, infill settings, and annealing condition. By implementing the filament-specific optimization framework established in this study beyond the scope of PLA, more efficient processing of new materials will be possible for enhanced applicability of FFF in the 3DP field. Full article

Process sustainability vs. mechanical strength is a strong market-driven claim in Material Extrusion (MEX) Additive Manufacturing (AM). Especially for the most popular polymer, Polylactic Acid (PLA), the concurrent achievement of these opposing goals may become a puzzle, especially since MEX 3D-printing offers a variety of process parameters. Herein, multi-objective optimization of material deployment, 3D printing flexural response, and energy consumption in MEX AM with PLA is introduced. To evaluate the impact of the most important generic and device-independent control parameters on these responses, the Robust Design theory was employed. Raster Deposition Angle (RDA), Layer Thickness (LT), Infill Density (ID), Nozzle Temperature (NT), Bed Temperature (BT), and Printing Speed (PS) were selected to compile a five-level orthogonal array. A total of 25 experimental runs with five specimen replicas each accumulated 135 experiments. Analysis of variances and reduced quadratic regression models (RQRM) were used to decompose the impact of each parameter on the responses. The ID, RDA, and LT were ranked first in impact on printing time, material weight, flexural strength, and energy consumption, respectively. The RQRM predictive models were experimentally validated and hold significant technological merit, for the proper adjustment of process control parameters per the MEX 3D-printing case. Full article