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Understanding the effect of process parameters on interfaces and interfacial bonding between two materials during multi-material additive manufacturing (MMAM) is crucial to the fabrication of high-quality and strong multi-material structures. The purpose of this paper is to conduct an experimental and statistical study to investigate the effect of process parameters of soft and hard materials on the flexural behavior of multi-material structures fabricated via material extrusion-based MMAM.

Sandwich beam samples composed of a soft core and hard shells are fabricated via MMAM under different printing conditions. A design of experiments is conducted to investigate the effect of the print speed and nozzle temperature on the flexural behavior of soft-hard sandwich beams. Analysis of variance and logistic regression analysis are used to analyze the significance of each process parameter. The interfacial morphology of the samples after the flexural tests is characterized. Thermal distributions during the MMAM process are captured to understand the effect of process parameters on the flexural behavior based on inter-bonding formation mechanisms.

Experimental results show that the soft-hard sandwich beams exhibited two different failure modes, including shell failure and interfacial failure. A transition of failure modes from interfacial failure to shell failure is observed as the nozzle temperatures increase. The samples that fail because of interfacial cracking exhibit a pure adhesive failure because of weak interfacial fracture properties. The samples that fail because of shell cracking exhibit a mixed adhesive and cohesive failure. The flexural strength and modulus are affected by the nozzle temperature for the hard material and the print speeds for both hard and soft materials significantly.

This paper first investigates the effect of process parameters for soft and hard materials on the flexural behavior of additively manufactured multi-material structures. Especially, the ranges of the selected process parameters are distinct, and the effect of all possible combinations of the process parameters on the flexural behavior is characterized through a full factorial design of experiments. The experimental results and conclusions of this paper provide guidance for future research on improving the interfacial bonding and understanding the failure mechanism of multi-material structures fabricated by MMAM.

Currently on additive manufacturing, extensive research is directed toward mitigating the main challenges associated with multi-material in fused filament fabrication which has a weak bonding strength between dissimilar materials. Low interfacial bonding strength leads to defects, anisotropy and temperature gradient in materials which negatively impact the mechanical performance of the multi-material prints. The purpose of this study was to assess the performance of different interface geometry designs in terms of the mechanical properties of the specimens.

Tensile test specimens were printed using: mono-material without a boundary interface, mono-material with the interface geometries (Face-to-face; U-shape; T-shape; Dovetail; Encapsulation; Mechanical interlocking; and Overlap) and multi-material with the interface geometries. The materials chosen with high and low compatibility were Tough polylactic acid (PLA) and TPU.

The main results of this study indicate that the interface geometries with the mechanical constriction between materials provide better structural integrity to the specimens. Moreover, in the case of the mono-material parts, the most effective interface design was the mechanical interlocking for both Tough PLA and TPU. On the other hand, in the case of multi-material specimens, the encapsulation showed the highest ultimate tensile strength, whereas the overlap and T-shape presented more robust bonding.

This study examines the mechanical performance, particularly tensile strength, strain at break, Young’s modulus and yield strength of different interface designs which were not studied in the previous studies.

The purpose of this study is to explore the synthesis of novel conductive photo-resins to produce flexible conducting composites for use in additive manufacturing. By using direct ink writing (DIW) additive manufacturing, this study aims to explore the fabrication of multimaterial devices with conductive and insulating components. Using digital light processing (DLP) additive manufacturing, this study aims to fabricate detailed objects with higher resolution than material extrusion 3D printing systems.

In this paper, several photocurable conducting resins were prepared for DIW and DLP additive manufacturing. These resins were then cured using 405 nm near UV light to create intrinsically conductive polymer (ICP) composites. The electrochemical properties of these composites were analysed, and the effect of co-monomer choice and crosslinking density was determined. These results determined a suitable resin for subsequent additive manufacture using DIW and DLP. These 3D printing techniques were used to develop flexible conducting devices of submillimetre resolution that were fabricated with unmodified, commercially available 3D printers.

Cyclic voltammetry and volume conductivity analysis of the conducting resins determined the most conductive resin formula for 3D printing. Conductive devices were fabricated using the two 3D printing techniques. A multimaterial soft conducting device was fabricated using DIW, and each conducting component was insulated from its neighbours. DLP was used to fabricate a soft conducting device with good XY resolution with a minimum feature size of 0.2 mm. All devices were prepared in unmodified commercially available 3D printers.

These findings have value in the development of soft robotics, artificial muscles and wearable sensors. In addition, this work highlights techniques for DIW and DLP additive manufacturing.

Several original conducting resin formulae were developed for use in two 3D printing systems. The resulting 3D-printed composites are soft and flexible while maintaining their conductive properties. These findings are of value to both polymer chemists and to the field of additive manufacturing.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

The development of new advanced materials, such as photopolymerizable resins for use in stereolithography (SLA) and Ti6Al4V manufacture via selective laser melting (SLM) processes, have gained significant attention in recent years. Their accuracy, multi-material capability and application in novel fields, such as implantology, biomedical, aviation and energy industries, underscore the growing importance of these materials. The purpose of this study is oriented toward the application of new advanced materials in stent manufacturing realized by 3D printing technologies.

The methodology for designing personalized medical devices, implies computed tomography (CT) or magnetic resonance (MR) techniques. By realizing segmentation, reverse engineering and deriving a 3D model of a blood vessel, a subsequent stent design is achieved. The tessellation process and 3D printing methods can then be used to produce these parts. In this context, the SLA technology, in close correlation with the new types of developed resins, has brought significant evolution, as demonstrated through the analyses that are realized in the research presented in this study. This study undertakes a comprehensive approach, establishing experimentally the characteristics of two new types of photopolymerizable resins (both undoped and doped with micro-ceramic powders), remarking their great accuracy for 3D modeling in die-casting techniques, especially in the production process of customized stents.

A series of analyses were conducted, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, mapping and roughness tests. Additionally, the structural integrity and molecular bonding of these resins were assessed by Fourier-transform infrared spectroscopy–attenuated total reflectance analysis. The research also explored the possibilities of using metallic alloys for producing the stents, comparing the direct manufacturing methods of stents’ struts by SLM technology using Ti6Al4V with stent models made from photopolymerizable resins using SLA. Furthermore, computer-aided engineering (CAE) simulations for two different stent struts were carried out, providing insights into the potential of using these materials and methods for realizing the production of stents.

This study covers advancements in materials and additive manufacturing methods but also approaches the use of CAE analysis, introducing in this way novel elements to the domain of customized stent manufacturing. The emerging applications of these resins, along with metallic alloys and 3D printing technologies, have brought significant contributions to the biomedical domain, as emphasized in this study. This study concludes by highlighting the current challenges and future research directions in the use of photopolymerizable resins and biocompatible metallic alloys, while also emphasizing the integration of artificial intelligence in the design process of customized stents by taking into consideration the 3D printing technologies that are used for producing these stents.

Three-dimensional (3D) printing has great potential in the food industry. While 3D printing technology offers customised food products to consumers, it also allows producers to develop new products using a wide variety of alternative food ingredients, modernise the production process and carry out environmentally friendly production. This research aims to determine the attitudes of students towards 3D foods who are studying in the Department of Gastronomy and Culinary Arts, as they are both consumers and examine different food processing systems and use them in the field of application. As a result of the study, it was identified that the participants believed that 3D printing is a great modern technology that allows the development of new foods, that it will bring benefit to us in the future, reduce the cost of food and food waste, increase the sustainability of food and that they see it as environmentally friendly. In addition, it was determined that the participants did not think that 3D-printed foods were disgusting; they found these foods reliable, could try them in the future and were excited to experience them.

This paper aims to investigate the current state of biocomposites used in fused deposition modelling (FDM) with a focus on their mechanical characteristics.

The study presents a variety of biocomposite materials that have been used in filaments for 3D printing by different researchers. The process of making filaments is then described, followed by a discussion of the process parameters associated with the FDM.

To achieve better mechanical properties of 3D-printed parts, it is essential to optimize the process parameters of FDM while considering the characteristics of the biocomposite material. Polylactic acid is considered the most promising matrix material due to its biodegradability and lower cost. Moreover, the use of natural fibres like hemp, flax and sugarcane bagasse as reinforcement to the polymer in FDM filaments improves the mechanical performance of printed parts.

The paper discusses the influence of critical process parameters of FDM like raster angle, layer thickness, infill density, infill pattern and extruder temperature on the mechanical properties of 3D-printed biocomposite.

This paper aims to study the effects of inorganic CaCO₃ nanoadditives in the polylactic acid (PLA) matrix and fused filament fabrication (FFF) process parameters on the mechanical characteristics of 3D-printed components.

The PLA filaments containing different levels of CaCO₃ nanoparticles have been produced by mix-blending/extrusion process and were used to fabricate tensile and three-point bending test samples in FFF process under various sets of printing speed (PS), layer thickness (LT), filling ratio (FR) and printing pattern (PP) under a Taguchi L27 orthogonal array design. The quantified values of mechanical characteristics of 3D-printed samples in the uniaxial and the three-point bending experiments were modeled and optimized using a hybrid neural network/particle swarm optimization algorithm. The results of this hybrid scheme were used to specify the FFF process parameters and the concentration of nanoadditive in the matrix that result in the maximum mechanical properties of fabricated samples, individually and also in an accumulative response scheme. Diffraction scanning calorimetry (DSC) tests were conducted on a number of samples and the results were used to interpret the variations observed in the response variables of fabricated components against the FFF parameters and concentration of CaCO₃ nanoadditives.

The results of optimization in an accumulative scheme showed that the samples of linear PP, fabricated at high PS, low LT and at 100% FR, while containing 0.64% of CaCO₃ nanoadditives in the matrix, would possess the highest mechanical characteristics of 3D-printed PLA components.

FFF is a widely accepted additive manufacturing technique in production of different samples, from prototypes to the final products, in various sectors of industry. The incorporation of chopped fibers and nanoparticles has been introduced recently in a few articles to improve the mechanical characteristics of produced components in FFF technique. However, the effectiveness of such practice is strongly dependent on the extrusion parameters and composition of polymer matrix.

To explore the influence of various factors on the adhesion strength of 3D printing materials and chiffon fabrics, and to develop an original design clothing prototype with an extended functionality that would be compatible with the specifics of the circular design.

Four different chiffon fabrics and four 3D printed materials were chosen as the research subjects to determine the influence of various factors on the adhesion strength and ductility. The uniaxial tensile test was used to determine pull-out force and the pull-out elongation from the interlayer.

3D printed TPU elements can be used to join clothing parts made from low-elasticity chiffon fabrics to improve wearing comfort. In order to comply with the circular economy concept, it is important to select such adhesion parameters of the 3D printed elements and the material system that would ensure wear comfort and withstand wear-level loads; and at the end of the life cycle of a garment, the 3D printed elements could be separated from the product and recycled.

The systems developed can be used to renew and repair products, adding originality, individual touch or additional decorative features, while extending the functional possibilities of clothing items in accordance with circular design principles.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.