Printing Process Research Articles

Facile Access to Highly Efficient 3D Printing Using Robust Self-Healing CDs/Polymer Hybrids.

3D printing efficiency, as a key indicator of additive manufacturing technology, directly affects its competitiveness in rapid prototyping, small batch production, and even large-scale industrial applications. Compared with traditional manufacturing methods, the high efficiency of 3D printing is often considered a bottleneck, hindering its application across various fields. Herein, a versatile and efficient strategy is proposed, namely, the dimensional reduction printing (DRP) process, to break the obstacle of high efficiency of 3D printing. Specifically, the self-healing CDs/PMMA nanocomposites were constructed via introducing carbon dots (CDs) synthesized by microfluidics into poly(methyl methacrylate) (PMMA) materials. Under the assistance of abundant hydrogen bonding and the entanglement of polymer chains, the fabricated CDs/PMMA nanocomposites exhibited outstanding self-healing properties, which can be utilized as ideal printing inks to achieve a highly efficient 3D printing process through assembling the printed simple 1D and 2D components into complex 3D models. We firmly believe that the DRP strategy opens up an idea for the design of a 3D printing process and points out the direction for the meaningful application of 3D printing technologies.

Experimental Evidence on the Possible Use of Fine Concrete and Brick Recycled Aggregates for 3D Printed Cement-Based Mixtures

In recent years, the development of alternative and more sustainable technologies for reinforced concrete structures has been attracting more and more interest, given the increasing need to reduce the impact that the construction sector has on the environment. Furthermore, 3D concrete printing (3DCP) technology falls into this context, allowing the optimization of the quantities of employed raw material to be used while at the same time allowing the possibility to design more complex elements’ shapes. In the view of improving the sustainability of construction sector, the present study aims at experimentally investigating the characteristics of the fresh and hardened states of concrete mixtures incorporating different percentages of replacement of the fine aggregate with recycled aggregates of different nature. As such, the key innovative aspect of the present study is the possible investigation of cement-based mixtures produced with 100% recycled fine aggregates (both derived from concrete waste and brick elements) without affecting either the fresh or hardened mechanical properties of the resulting Recycled Aggregate Concrete (RAC) mixtures. Furthermore, in order to make this study linked to 3D printing technology, extruded concrete elements were realized and tested through a process designed to simulate the automated 3D concrete printing process: in fact, the RAC mixtures were designed in order to obtain an adequate workability and compressive strength typically adopted for ordinary 3D printed mixtures. Although some adjustments and further analyses are required in order to optimize the shape retention and stability, as well as the well-known problem of the 3D mixtures being linked to anisotropic behavior, the obtained results unveil that it was possible to observe promising characteristics for the mixes containing recycled aggregates (i.e., consistency index at the fresh state above 150 mm and compressive strength at 28 days above 50 MPa), which were in any case suitable for the creation of 3D printed structural concrete elements and can be further confirmed with future studies in order to validate their possible buildability.

A color comparison between inkjet and dye sublimation on non-paper substrates after environmental exposure

This study originated with a need to achieve archival quality for full color, fine art photography and art when printed on non-traditional substrates. In order to better understand how environmental factors affect color fastness and accuracy across two common photo production processes–dye sublimation and inkjet–the authors compared color shifts on targets printed on glass, metal, tile, and wood after exposure to humidity or full spectrum UV light. After coating and printing on the substrates, a spectrophotometer was used to record pre-exposure LAB readings from the printed targets. Post-exposure readings were recorded using the same device after each sample was exposed to 500 consecutive hours of either humidity or UV light. A regression analysis and paired T-tests were used to analyze the shift in color after environmental exposure across substrates and printing processes. Before environmental exposure, the authors found that dye sublimation, especially on metal substrates, produced visually richer colors compared with the inkjet process. This observation was confirmed quantitatively when all substrates printed with inkjet produced lower average densities than those printed using dye sublimation, with wood and metal having overall higher color densities than glass and tile. With regards to substrate performance, tile printed with either process performed similarly across both environmental conditions with minimal degradation during exposure. Dye sublimated wood under UV and inkjet-printed metal exposed to humidity degraded the most significantly across all the variable combinations. The authors concluded that even though some process and substrate combinations perform better than others, exposure to certain environmental factors can lead to significant color degradation and therefore, fine art prints should be protected to ensure longevity when there is an expected environmental condition. However, an artist or collector can choose substrate and print process combinations that are more likely to withstand environmental conditions yet still provide the intended visual impact.

Beta‐Lactoglobulin for Water‐Based and Tunable Nanostructure Templating of Printed Titania Thin Films: The Influence of pH Value and Protein Concentration

AbstractAn environmentally friendly as well as scalable synthesis route of nanostructured titania thin films is of interest for many state‐of‐the‐art devices, from solar cells to battery materials. Beta‐lactoglobulin (ß‐lg) enables water‐based and tunable titania thin film templating, allowing for different domain sizes, porosities, and morphologies. When printed with a slot‐die coater, the titania films can be tailored to specific applications with simple changes to the solution chemistry. Films printed at acidic pH conditions form significantly different final morphologies than films printed at a neutral pH value. The protein concentration plays a more limited role in the final nanostructure. With in situ grazing incidence small‐angle/wide‐angle X‐ray scattering (GISAXS/GIWAXS), the structure formation is followed with an excellent time resolution during the printing process. From the GISAXS measurements, the size evolution of the titania clusters is understood, showing significant differences for different pH values. Crystal phases and corresponding crystal orientations are investigated with GIWAXS. The combination of a water‐based titania synthesis with the scalable film deposition via slot die coating makes the presented results interesting for potential environmentally friendly mass production of nanostructured titania films.

Characteristics of Food Printing Inks and Their Impact on Selected Product Properties

Three-dimensional printing, or additive manufacturing, produces three-dimensional objects using a digital model. Its utilisation has been observed across various industries, including the food industry. Technology offers a wide range of possibilities in this field, including creating innovative products with unique compositions, shapes, and textures. A significant challenge in 3D printing is the development of the optimal ink composition. These inks must possess the appropriate rheology and texture for printing and meet nutritional and sensory requirements. The rheological properties of inks play a pivotal role in the printing process, influencing the formation of stable structures. This article comprehensively characterises food inks, distinguishing two primary categories and their respective subgroups. The first category encompasses non-natively extrudable inks, including plant-based inks derived from fruits and vegetables and meat-based inks. The second category comprises natively extrudable inks, encompassing dairy-based, hydrogel-based, and confectionary-based inks. The product properties of rheology, texture, fidelity, and printing stability are then discussed. Finally, the innovative use of food inks is shown.

A systematic approach to evaluate jetability of high-viscosity resins for 3D inkjet printing applications

Abstract Recent advances in piezo inkjet printing technology have enabled the use of high-viscosity materials previously unsuitable for inkjet applications. This technology facilitates the printing of vat photopolymerization resins, traditionally used to produce single-material components with superior properties compared to those achieved with conventional UV inks in material jetting. The ability to print these resins with piezo inkjet technology opens new fields of application for producing multimaterial functional parts. To fully exploit this potential, it is essential to evaluate the compatibility of these resins with the new technology. This study systematically evaluates the jetability, printability, and performance of high-viscosity materials to optimize the printing process and provides a detailed understanding of the factors influencing the jetability of high-viscosity resins and develops guidelines for optimizing their use in 3D inkjet printing applications.

Volumetric bioprinting of the osteoid niche

Volumetric bioprinting has revolutionized the field of biofabrication by enabling the creation of cubic centimeter-scale living constructs at faster printing times (in the order of seconds). However, a key challenge remains: developing a wider variety of available osteogenic bioinks that allow osteogenic maturation of the encapsulated cells within the construct. Herein, the bioink exploiting a step-growth mechanism (norbornene-norbornene functionalized gelatin in combination with thiolated gelatin-GelNBNBSH) outperformed the bioink exploiting a chain-growth mechanism (gelatin methacryloyl-GelMA), as the necessary photo-initiator concentration was three times lower combined with a more than 50% reduction in required light exposure dose resulting in an improved positive and negative resolution. To mimic the substrate elasticity of the osteoid, two concentrations of the photo-initiator Li-TPO-L (1 and 10 mg ml-1) were compared for post-curing whereby the lowest concentration was selected since it resulted in attaining the osteogenic substrate elasticity combined with excellent biocompatibility with HT1080 cells (>95%). Further physico-chemical testing revealed that the volumetric printing (VP) process affected the degradation time of the constructs with volumetric constructs degrading slower than the control sheets which could be due to the introduced fibrillar structure inherent to the VP process. Moreover, GelNBNBSH volumetric constructs significantly outperformed the GelMA volumetric constructs in terms of a 2-fold increase in photo-crosslinkable moiety conversion and a 3-fold increase in bulk stiffness of the construct. Finally, a 21-day osteogenic cell study was performed with highly viable dental pulp-derived stem cells (>95%) encapsulated within the volumetric printed constructs. Osteogenesis was greatly favored for the GelNBNBSH constructs through enhanced early (alkaline phosphatase activity) and late maturation (calcium production) osteogenic markers. After 21 d, a secretome analysis revealed a more mature osteogenic phenotype within GelNBNBSH constructs as compared to their chain-growth counterpart in terms of osteogenic, immunological and angiogenic signaling.

An experimental approach for dimensional tolerance analysis using 3D printed parts

Abstract In 2D or 3D tolerance analysis, a dimension chain involves a complex propagation of random deviations from individual parts to the controlled assembly requirement. This can be calculated by setting up complex mathematical models, possibly with the help of CAD-based software tools. The paper studies the feasibility of an alternative approach, which uses measurements on assemblies of 3D printed parts to estimate the coefficients of a linearized error propagation model. The analysis is structured as a designed experiment, where each dimension is varied in two levels resulting in two different printed parts. The experimental plan consists of building assemblies from selected combinations of parts and measuring the assembly dimension on each combination. Among the possible designs of the plan, the paper compares two methods based on finite differences and least squares. The comparison includes both simulations with randomly generated dimension chains and physical tests on simple cases. The results show that a least squares plan allows a significant improvement in the accuracy on the estimation of model coefficients compared to a finite difference plan, with just a marginally higher number of assembly combinations. However, dimensional and geometric deviations in the 3D printing process are shown to play a critical role, and criteria are suggested to reduce or compensate them.

Biomimetic 3D Prototyping of Hierarchically Porous Multilayered Membranes for Enhanced Oil-Water Filtration.

This study introduces a biomimetic approach to 3D printing multilayered hierarchical porous membranes (MHMs) using Direct Ink Writing (DIW) technology. Fabricated through a fast layer-by-layer printing process with varying concentrations of pore-forming agents, the produced MHMs mimic the hierarchical pore structure and filtration capabilities of natural soil systems. As a result, the 3D-printed MHMs achieved an impressive oil rejection rate of 99.02% and demonstrated exceptional reusability, maintaining a flux recovery ratio of 99.48% even after hours of continuous filtration. Moreover, the 3D-printed MHMs exhibit superior hierarchical porous architecture and mechanical integrity compared to traditional flat sheet single-layered membranes. This study presents a significant advancement for scalable 3D printing of customized multilayer membranes with tailored porosity and high-performance filtration properties. The simplicity, versatility, and cost-effectiveness of the presented manufacturing method offer a pathway for advanced design and on-demand membrane production.

Математическое моделирование нестационарного теплопереноса в селективном лазерном плавлении на основе машинного обучения

The article considers numerical modeling of thermal processes in 3D printing using selective laser melting technology based on machine learning. A mathematical model of non-stationary heat transfer in a rod with a variable cross sec-tion is developed as a partial differential equation describing the rod's temperature. An algorithm for numerically solv-ing this equation is proposed, implemented using the Matlab system. It is shown that, for certain initial conditions, the temperature distribution becomes quasi-stationary, and for this case, a simple analytical expression for the temperature field is obtained. A neural network is constructed and trained using the TensorFlow library, with training data obtained from the analytical solution of the thermal problem. The neural network's calculation results align with the solutions of the original mathematical model. The article highlights that three-dimensional modeling of the printing process for real-world products demands substantial computational resources. It is shown that machine learning-based models can effectively approximate the temperature field in 3D printing with selective laser melting technology for components of similar geometry.

Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing

Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation. In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. Three representative 2D shapes at three different scales are used as test cases. In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effort for optimization.

Mechanical performance optimization in FFF 3D printing using Taguchi design and machine learning approach with PLA/walnut Shell composites filaments

AbstractThis study explores the optimization of mechanical properties in 3D‐printed components made from a Polylactic Acid (PLA) and Walnut Shell Composite using Fused Filament Fabrication (FFF). Employing a machine learning‐based approach, the research identifies the optimal regression model for predicting relationships between printing parameters and material properties. A Taguchi L18 design is used to minimize experiment count while accurately determining parameter levels. Testing was conducted on a composite containing 30% walnut shell fibers, with the Ultimate Tensile Strength (UTS) and Elastic Modulus (E) measured as per ASTM D638 standards. Experimental factors included Layer Thickness (LT), Nozzle Temperature (NT), Deposition Angle (DA), and Printing Speed (PS). Using Analysis of Variance (ANOVA) and machine learning techniques, the effects of these parameters on UTS and E were evaluated. Results highlight the deposition angle as the dominant parameter, with machine learning models, especially Random Forest Regression, providing highly accurate predictions. This approach presents a novel, data‐driven method for optimizing 3D printing processes with sustainable, composite materials.Highlights Higher UTS and E achieved with optimized PLA/walnut shell composite. Deposition angle is the key element of mechanical performance in FFF printing. Layer thickness is important to improve Elastic Modulus. Statistical and machine learning techniques combined for sustainable printing. Improved machine learning process understanding for 3D printed components.

An Adaptive Spatial-Terminal Iterative Learning Strategy in Roll-to-Roll Control Problems

Abstract Roll-to-Roll (R2R) systems, featuring motorized or idle rollers, are crucial for high-volume, continuous production of flexible substrates. A significant challenge in R2R printing processes is maintaining tight alignment tolerances for multi-layer printed electronics. This alignment, known as registration, is complicated by the deformability of flexible substrates and complex roller dynamics, leading to registration errors (RE) caused by variations in substrate tensions and speeds. Despite using real-time feedback controllers like PID or MPC for tension control, these systems struggle with transient and angle-periodic disturbances common in R2R systems. We introduce a Spatial-Terminal Iterative Learning Control (STILC) method with an adaptive basis function to eliminate RE in R2R gravure printing. This function, along with the registration error, updates the STILC compensation profile via a P-type Iterative Learning Control law. Our numerical experiments demonstrate that STILC with the adaptive basis function effectively eliminates RE caused by roller-motor axis mismatches and provides better convergence than STILC with an invariant basis function. This novel approach shows promise for various industrial applications involving spatially periodic disturbances, particularly those with unknown dynamics and without given state trajectories to track rigorously, which is common in engineering practice.

Dynamic Reconstruction of Fluid Interface Manipulated by Fluid Balancing Agent for Scalable Efficient Perovskite Solar Cells.

Laboratory-scale spin-coating techniques are widely employed for fabricating small-size, high-efficiency perovskite solar cells. However, achieving large-area, high-uniformity perovskite films and thus high-efficiency solar cell devices remain challenging due to the complex fluid dynamics and drying behaviors of perovskite precursor solutions during large-area fabrication processes. In this work, a high-quality, pinhole-free, large-area FAPbI3 perovskite film is successfully obtained via scalable blade-coating technology, assisted by a novel bidirectional Marangoni convection strategy. By incorporating methanol (MeOH) as a fluid balance agent, the direction of Marangoni convection is effectively regulated, mitigating the disordered motion of colloidal precursor particles during the printing process. As a result, champion power conversion efficiencies (PCEs) of 24.45% and 20.32% are achieved for small-area FAPbI3 devices (0.07cm2) and large-area modules (21cm2), respectively. Notably, under steady illumination, the device reached a stabilized PCE of 24.28%. Furthermore, the unencapsulated device exhibited remarkable operational stability, retaining 92.03% of its initial PCE after 1800h under ambient conditions (35±5% relative humidity, 30°C). To demonstrate the universality of this strategy, a blue perovskite light-emitting diode is fabricated, showing an external quantum efficiency (EQE) of 14.78% and an electroluminescence wavelength (EL) of 494nm. This work provides a significant technique for advancing solution-processed, industrial-scale production of high-quality and stable perovskite films and solar cells.

Silicone Rheological Properties for Material Extrusion Additive Manufacturing

ABSTRACTAdditive manufacturing (AM), known as three‐dimensional (3D) printing, uses computer‐controlled materials deposition to fabricate 3D objects by selectively depositing materials, usually in a layer‐wised fashion, to build a 3D object using free‐form fabrication. Integrating silicone elastomers with AM deposition strategies has been of interest due to the important application characteristics of silicones such as excellent mechanical properties, thermal resistance, and chemical inertness. This work presents a study on the shear‐thinning properties of thermally‐curable liquid silicone feedstocks to describe ideal flow and shape‐retention properties for direct ink writing of liquid silicone rubbers. To complement the direct ink writing process developed in this work for silicone AM, flow properties of various silicone feedstocks were identified through measurement of rheological properties using the AM fluid dispenser under various pressures, supported by parallel plate oscillatory shear rheology. A systematic process for evaluating and investigating the AM performance of seven different grades of silicones is introduced. The shape retention, overhang, and dimensional accuracy of these silicones in 3D printing process have been compared and summarized. This systematic evaluation methodology can be applied for silicone material selection and printing of silicone parts with complicated architectures.

Design and implementation of virtual reality instructional tools for 3D printing processes in digital manufacturing

Design and implementation of virtual reality instructional tools for 3D printing processes in digital manufacturing

Enhancing charge carrier dynamics with an N-type polymer guest for printable ternary organic solar modules

The ternary strategy offers a promising route to enhance the power conversion efficiencies (PCEs) of organic solar cells (OSCs). In this contribution, we focus on the state-of-the-art binary system PM6:L8-BO and reveal how the n-type polymer guest (PYIT) in the PM6:L8-BO:PYIT ternary system enhances carrier dynamics, thereby improving both efficiency and scalability for large-area printable OSC modules. These benefits are primarily attributed to two key factors: (i) the excellent miscibility of the PYIT guest with the host materials, coupled with the chain-dominant structure and high crystallinity nature of the PYIT polymer, which creates additional pathways for carrier transport and charge transfer; (ii) the incorporation of PYIT, which limits the excessive aggregation of L8-BO, improves molecular packing, and reduces film defects, thereby enhancing exciton dynamics. These optimizations lead to an increase in PCEs from 17.58% in the binary system to 18.59% in the ternary OSC, with improvements across all photovoltaic parameters. More importantly, the PM6:L8-BO:PYIT ternary system exhibits excellent compatibility with large-area printing processes, as demonstrated by a doctor blading OSC module achieving 15.57% PCE over an area of 11.7 cm2. This work highlights the potential of the ternary methodology in tuning the physical properties of OSCs while enhancing both performance and scalability.

Influence of heat-assisted vat photopolymerization on the physical and mechanical characteristics of dental 3D printing resins

The effects of heat-assisted vat photopolymerization (HVPP) on the physical and mechanical properties of 3D-printed dental resins, including the morphometric stability of 3D-printed crowns, were investigated. A resin tank was designed to maintain the resin at 30, 40, and 50 ℃ during the 3D printing process. Test specimens were fabricated using a commercial dental resin, with untreated resin serving as the control group. Key properties such as viscosity, curing kinetics, surface microhardness, flexural properties, and dimensional accuracy were evaluated. The viscosity of the resin decreased significantly (P < 0.05) with increasing temperature, thereby enhancing its flow properties. Photo-DSC analysis revealed a 17.58% increase in peak heat flow at 50 ℃, indicating accelerated polymerization. Surface microhardness improved significantly (P < 0.05) with HVPP, though a slight reduction was observed at 50 ℃ compared to that at 30 and 40 ℃. The flexural strength, modulus, and resilience were significantly enhanced (P < 0.05) at higher temperatures, with 50 ℃ yielding the best mechanical properties. However, 3D morphometric analysis showed increased root mean square deviation from the CAD design at elevated temperatures. Our results suggest that HVPP enhances the durability of dental prostheses, although careful optimization of the printing temperature is essential to balance their strength and accuracy.

A Wenzel Interfaces Design for Homogeneous Solute Distribution Obtains Efficient and Stable Perovskite Solar Cells.

The coffee-ring effect, caused by uneven deposition of colloidal particles in perovskite precursor solutions, leads to poor uniformity in perovskite films prepared through large-area printing. In this work, the surface of SnO2 is roughened to construct a Wenzel model, successfully achieving a super-hydrophilic interface. This modification significantly accelerates the spreading of the perovskite precursor solution, reducing the response delay time of perovskite colloidal particles during the printing process. Additionally, the micro-spherical depression structure on the SnO2 surface effectively inhibits the migration of colloidal particles toward the edges of liquid film, trapping perovskite colloidal particles at the buried interfaces and improving film uniformity. Due to the synergistic effect of super-hydrophilicity and micro-rough structure on the surface of SnO2, leading to a substantial improvement in the quality of perovskite crystals. Therefore, the efficiency of printing prepared flexible devices (0.101 cm2) reached 25.42% (certified 25.12%). Moreover, the efficiency of rigid and flexible large-scale perovskite solar modules (PSMs) based on meniscus-coating manufacture reached 21.34% and 16.99% (100 cm2), respectively, and demonstrated superior environmental stability by maintaining an initial efficiency of 91% after being stored in atmospheric conditions for 2000 h, offering practical guidance for fabricating high-performance and stable large-scale perovskite solar cells (PSCs).

Laboratory validation of patient-specific templating for total knee arthroplasty

Patient-specific templating (PST), which is a sister procedure to patient-specific instrumentation (PSI) but hospital-based, is relatively less complex and less expensive than robotics and navigation. However, there are some concerns about the PST including the process of preoperative planning, 3D printing and material, positioning of PST intraoperatively, availability, and clinical value. The purpose of this study was to validate the technical accuracy and reliability of the PST technique in the lab and to report the outcomes of clinical application. To test the reliability of the PST technique, five observers positioned the PST templates five times over the distal femur and proximal tibial whilst a navigation system was used to measure the level of bone cutting, coronal and sagittal alignment, and rotation in both femur and tibia. The mean alignment error in all planes was 0.67° (maximum 2.5°). Concerning the bone (femoral and tibial) cutting, the mean error was 0.32 mm (maximum 1 mm). The qualitative and quantitative analysis showed an overall agreement between observers (p < 0.05). The laboratory part of this study showed that the positioning of the PST over the proximal tibia and distal femur during TKA is reliable. There were statistically insignificant intraobserver and interobserver variations.