Printing Process Research Articles

Engineered Shape-Morphing Transitions in Hydrogels Through Suspension Bath Printing of Temperature-Responsive Granular Hydrogel Inks.

4D printing of hydrogels is an emerging technology used to fabricate shape-morphing soft materials that are responsive to external stimuli for use in soft robotics and biomedical applications. Soft materials are technically challenging to process with current 4D printing methods, which limits the design and actuation potential of printed structures. Here, a simple multi-material 4D printing technique is developed that combines dynamic temperature-responsive granular hydrogel inks based on hyaluronic acid, whose actuation is modulated via poly(N-isopropylacrylamide) crosslinker design, with granular suspension bath printing that provides structural support during and after the printing process. Granular hydrogels are easily extruded upon jamming due to their shear-thinning properties and their porous structure enables rapid actuation kinetics (i.e., seconds). Granular suspension baths support responsive ink deposition into complex patterns due to shear-yielding to fabricate multi-material objects that can be post-crosslinked to obtain anisotropic shape transformations. Dynamic actuation is explored by varying printing patterns and bath shapes, achieving complex shape transformations such as 'S'-shaped and hemisphere structures. Furthermore, stepwise actuation is programmed into multi-material structures by using microgels with varied transition temperatures. Overall, this approach offers a simple method to fabricate programmable soft actuators with rapid kinetics and precise control over shape morphing.

Bio-inspired 3D printing of strain-hardening cementitious composites reticulated shell roofs

Inspired by the diving bell structure of the water spider, this study presents an innovative 3D printed strain-hardening cementitious composite (3DP-SHCC) reticulated shell roof. The compressive behavior of the 3DP-SHCC reticulated shell roof is evaluated using a combination of experimental tests and numerical simulation approaches. The 3DP-SHCC reticulated shell roof features excellent mechanical properties and an aesthetically pleasing appearance. The specific energy absorption and ductility index of this structure are 0.065 J/g and 0.078, respectively. The feasibility of this concept is validated by constructing a complete building structure within just 16 min. This printing process has the potential to create complex designs with minimal labor, making it suitable for rapid construction in extreme environments.

A Scalable Fully Printed Organic Thermoelectric Generator for Harsh Environments Enabled by a Stable n‐type Polymer

AbstractAffordable and versatile power sources become crucial as miniaturized electronics and sensors become more widespread. Organic thermoelectric generators (TEGs), capable of tapping into wasted heat sustainably and economically, are viewed as key enablers for powering the sensor networks of the future. Until recently, the performance of n‐type organic thermoelectric materials has severely lagged behind their p‐type counterparts. However, reports of a stable and highly conductive n‐type polymer, poly(benzodifurandione) (PBFDO), are changing this notion. Nonetheless, validation of PBFDO as a suitable ink for scalable fabrication processes and integration of the material into a scalable organic TEG have yet to be demonstrated. This work presents the development, characterization, and optimization of a PBFDO ink for use in a scalable inkjet printing process. The n‐type ink is then integrated into a screen‐printed origami‐inspired architecture, resulting in a 21‐leg PEDOT:PSS/PBFDO organic TEG capable of delivering a record‐high normalized power density among reported polymer‐based TEGs of 0.718 nW cm−2 K−2. Moreover, the module ambient stability is demonstrated for up to 90 d, while its use for potential applications in harsh environments is validated for a temperature range of ‐8 °C to 200 °C and up to 90% relative humidity, a first for organic TEGs.

Acoustic emission monitoring of a laser powder bed fusion process

Additive manufacturing (AM) metal parts offers opportunities for various industrial applications. From individual production of spare parts for unique mechanical components to prototyping of complex structures, the possibilities of production using the additive manufacturing process are manifold. One common AM technique is the Laser Powder Bed Fusion (PBF-LB/M) process, where a laser is used to selectively melt metal powder and create the parts layer wise as designed in a model. During manufacturing certain defects like pores, cracks and lack of fusion may be created in the built parts. As AM parts often have complex geometries, a postprocess non-destructive testing is difficult or even not possible. Thus, different optical monitoring techniques are applied to detect flaws during the build process with the aim to detect and repair defects right away during the manufacturing process. However, optical monitoring system require a clear view of the melt pool and quite expensive equipment. Acoustic monitoring by AE sensors attached to the built plate would be a cheaper alternative which also can be used without visual contact to the building chamber. This paper shows a first approach of an AE based process monitoring. The build plate of a PBF-LB/M machine therefore was adapted to hold four AE sensors that are then used to monitor the process. The build process itself creates AE signals caused by the melting and cooling and the mechanical application of powder. The formation of pores and cracks is expected to create additional acoustic emissions that will be distinct from the AE pattern from the printing process. This AE signals can be linked to the different layers of the printed part or in the long run to the laser position. Results from the first tests in a customized LPBF machine will be shown as well as the implementation of AE into the PBF-LB/M machine.

4D printing of liquid crystal elastomer composites with continuous fiber reinforcement

Multifunctional composites have been continuously developed for a myriad of applications with remarkable adaptability to external stimuli and dynamic responsiveness. This study introduces a 4D printing method for liquid crystal elastomer (LCE) composites with continuous fibers and unveils their multifunctional actuation and exciting mechanical responses. During the printing process, the relative motion between the continuous fiber and LCE resin generates shear force to align mesogens and enable the monodomain state of the matrix materials. The printed composite lamina exhibits reversible folding deformations that are programmable by controlling printing parameters. With the incorporation of fiber reinforcement, the LCE composites not only demonstrate high actuation forces but also improved energy absorption and protection capabilities. Diverse shape-changing configurations of 4D composite structures can be achieved by tuning the printing pathway. Moreover, the incorporation of conductive fibers into the LCE matrix enables electrically induced shape morphing in the printed composites. Overall, this cost-effective 4D printing method is poised to serve as an accessible and influential approach when designing diverse applications of LCE composites, particularly in the realms of soft robotics, wearable electronics, artificial muscles, and beyond.

Vat photopolymerization for thermal energy storage applications using encapsulated phase change material suspended in photocurable resin

A novel approach to additively manufacture latent heat thermal energy storage heat exchangers through the development of microencapsulated phase change material (MEPCM) suspensions in photocurable resin for vat photopolymerization (VPP) 3D printing is presented. Using MEPCM addresses the leakage risks that have typically been associated with PCMs and subsequently makes the particulates a suitable additive for VPP 3D printing. In the current study, VPP was employed to fabricate functional composites with varying MEPCM mass fractions for thermal, rheological, microstructure, and chemical characterization. Microstructure visualization was conducted to assess the overall distribution of MEPCM within the 3D printed samples and to confirm the structural integrity of the encapsulated particles after printing. The influence of the base resin viscosity was explored by investigating two photocurable resins with different viscosities—a high-tensile UV photopolymer and an ABS-like resin—during the printing process. Thermal properties, such as latent heat of fusion, phase-change temperature, thermal conductivity, and decomposition temperature of the 3D printed samples were determined. Rheology was used to observe the effect of varying shear rates on the MEPCM-resin mixtures to identify the optimal viscoelastic properties for VPP 3D printing. It was determined that the ABS-like resin was able to contain a larger amount of PCM (40 wt%) while maintaining printability due to the lower viscosity of the corresponding pure resin. The 40 wt% MEPCM composite exhibited an average viscosity of 18,817 cPs, a maximum latent heat of fusion of 54.12 kJ/kg, and a 12.4 % reduction in thermal conductivity compared to the pure polymer.

Machine learning predictions of spatter behavior in LPBF additive manufacturing

The adoption of additive manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (LPBF), has been rapidly increasing in industries requiring high precision and complex geometries. Despite its advantages, LPBF faces challenges related to defects that affect material quality, with spatter formation being a significant concern. Spatters—tiny particles ejected during the printing process—can adversely affect the final product’s integrity by altering surface roughness and contributing to defects. This study introduces a comprehensive approach to predict the ejection velocity and direction of spatter particles using a suite of machine learning (ML) algorithms, including Random Forest, Gaussian Process Regression, Support Vector Machine, Regularized Linear Regressions, Gradient Boosting Trees, and Neural Networks. Our analysis reveals that the Neural Network model outperforms others, achieving prediction accuracies of 97.58% for spatter velocity and 88.22% for ejection direction, thus offering a substantial improvement in understanding and controlling spatter-related defects in LPBF processes. The practical implications of these predictions are profound, enabling manufacturers to adjust AM parameters in real time to minimize defects and enhance product quality. This study not only fills a gap in the current literature by providing a detailed comparative analysis of multiple ML algorithms for spatter ejection prediction but also paves the way for future research into real-time monitoring and control systems in AM.

An analytical model for estimating process parameters input in L-DED based on bead geometry

Additive manufacturing of metal alloys via laser Directed Energy Deposition (L-DED) has been gaining popularity due to its potential to repair and create new features/components, enabling new applications for built parts. The success of L-DED operations hinges on the precise control of printing parameters, including laser power, scanning speed, and powder feed rate. These parameters significantly influence heat distribution during printing, directly impacting the quality of the resulting parts. Thus, defining an efficient methodology to find a good correlation between these parameters for the printing process is crucial to boost part production, as it reduces the time-consuming trial-and-error parameter tuning process. In this context, our study introduces an analytical model that predicts printing parameters based on the deposited material volume along track lines. Deposition was carried in stainless steel 316L with different values for laser power (ranging from 500 to 750W with 50W increments), scanning speed (from 400 to 700mm/min with 100mm/min increments), and powder feed rate (6.4,8.0 and 10.0g/min). The experimental data verified the effectiveness of the proposed model, demonstrating its potential to standardize the first step of printing process and expedite the initial search for optimal printing parameters in L-DED. The model provided accurate initial estimates of laser power, with a maximum relative error of 12%, particularly for the optimum mass flow rate (ṁ) of 8.0g/min. Beyond its benefits to the L-DED process, this analytical solution contributes to experimental practices by offering an efficient method for predicting material deposition volume during printing. Thus, our work underscores the significance of optimizing printing parameters to achieve high-quality parts and provides a valuable reference for future research and studies in the field of L-DED.

Chaotic dynamics of flexible graphene electronic membranes with variable density in motion

With advancements in artificial intelligence and wearable technology, flexible electronic devices characterized by their flexibility and extensibility have found widespread applications in fields such as information technology, healthcare, and military. Printing technology can accurately print a circuit diagram onto a flexible membrane substrate by the pressure transfer of a conductive ink, which makes the large-scale printing of flexible graphene electronic membranes possible. However, during the roll-to-roll printing process used to prepare flexible graphene electron membranes, the density of electron membranes is variable due to the uneven distribution of inkjet-printed circuits, which limits the printing speed of flexible graphene electron membranes. Hence, investigating the dynamic properties of flexible graphene electron materials with different densities is of paramount importance to improve the production efficiency and quality of flexible graphene electron membranes. This paper takes roll-to-roll intelligent graphene electronic membranes as the research object. According to Hamilton’s principle, nonlinear vibration partial differential equations for the motion of flexible graphene electron membranes with varying densities were established and subsequently discretized using the assumed displacement function and the Bubnov–Galerkin method. Through numerical calculations, the simulation results obtained based on the fourth-order Runge–Kutta method and the multiscale algorithm were compared, and the multiscale algorithm was verified to be more correct and effective. The primary resonance amplitude–parameter characteristic curve, along with phase-plane portraits, Poincaré maps, power spectrum, time history plots, and bifurcation diagrams, for the nonlinear behavior of the membrane was obtained. The impacts of the density coefficient, velocity, damping ratio, excitation force, and detuning parameters on the nonlinear primary resonance and chaotic behavior of the moving graphene electron membrane were determined, and the stable operational region was identified, laying a theoretical foundation for the development of flexible graphene electronic membranes.

Effects of Ultrasonic Vibration on 3D Printing of Polylactic Acid/Akermanite Nanocomposite Scaffolds

The mechanical properties of 3D-printed scaffolds for load-bearing implantation are crucial. Although the addition of nanoparticles to polymeric matrix scaffolds can improve their mechanical and biological properties, due to certain limitations in printability, high amounts of reinforcement cannot be used. Therefore, in this study, an attempt was made to use ultrasonic (UT) vibration to inhibit nozzle clogging during fused filament fabrication (FFF) of polylactic acid (PLA) scaffolds including 0, 20, and 40 wt.% Akermanite (Ak). Nozzle clogging happened during the conventional 3D printing of PLA-40wt.%Ak, while that did not occur during UT-assisted 3D printing of PLA-40wt.%Ak. Results of X-ray Diffraction (XRD) analysis and Scanning Electron Microscopy (SEM) indicated that applying ultrasonic (UT) vibration had no negative effect on the phases and morphology of the scaffolds. The results obtained by the compression test indicated that applying UT during 3D-printing resulted in almost 27% increment of elastic modulus and almost 25% increase of the compressive strength of the scaffolds. As the conclusion, this study highlights the effectiveness of ultrasonic-assisted 3D printing in producing nanocomposite scaffolds with significantly higher nanoparticle loadings, as compared to conventional printing methods. By utilizing ultrasonic vibration during the printing process, the study showcases the possibility of overcoming viscosity limitations and optimizing the mechanical performance of scaffolds for various biomedical applications, including bone tissue engineering.

Material extrusion additive manufacturing of bi-metal structures: A numerical and experimental study of interfacial microstructure

Bi-metal structures attract significant attention in research and industry due to their great performance and superior functionality. However, the formation of interface has not been comprehensively investigated due to the lack of methodologies to unveil the bonding mechanism between dissimilar materials. In this study, we developed a discrete element-based modeling and simulation to predict and understand the micro-scale bonding of stainless steel and nickel superalloys built by a printing, debinding, and sintering process. Two governing diffusion mechanisms were implemented to reveal the microstructural evolution from loose particles to a dense bi-metal component. The densification and grain growth of the interface were simulated and validated by experimental observations through scanning electron microscopy and electron backscatter diffraction analysis. Our simulation can accurately predict the interfacial microstructure, demonstrating a successful adoption of the modeling and simulation methodology to understand the interfacial formation of dissimilar materials shaped by AM and bonded by solid-state sintering.

Empowering decentralized production: A distributed manufacturing system for additive manufacturing processes

This work presents a pilot distributed manufacturing system for decentralized additive manufacturing, focusing on the Interdisciplinary Center for Advanced Manufacturing Systems (ICAMS) capabilities at Auburn University. The paper outlines the first phase of a multi-phase implementation plan and demonstrates the developed capabilities. The vision is to coordinate geographically separated manufacturing facilities as a cohesive unit. In our work, we prioritize instantaneous communication for file and information sharing. The Manufacturing Hub and Remote Cell, two facilities within driving distance, are coordinated in terms of part design, printing, and inspection procedures for a distributed Additive Manufacturing process. To enable coordination between the two facilities, ICAMS addresses three technological needs. These needs consider information sharing, process standardization, and data collection and monitoring. A network attached storage facilitates information sharing, a database-connected graphical user interface streamlines and standardizes the manufacturing process, and a data collection system facilitates process monitoring. The graphical user interface allows for order placement, progress monitoring, and communication updates. The developed phased plan, collaborative workflow, technological solutions, and production protocols demonstrate the potential for decentralized additive manufacturing. Successful integration of communication, design, printing, and quality assurance processes sets the foundation for future advancements in decentralized manufacturing systems.

Predictive models for 3D inkjet material printer using automated image analysis and machine learning algorithms

Additive manufacturing (AM) is a smart manufacturing process to fabricate components with high precision, minimal post-processing, and increased component complexity in a variety of materials. This research focuses on developing automated image analysis and predictive models for a widely used 3D material inkjet printing (IJP) process. The interplay of four input process parameters, which include frequency, voltage, temperature, and meniscus vacuum, on the output metrics of the inkjet printer was evaluated using statistical measures (ANOVA). Droplet types were classified as no drop, satellite drop, and normal drop using four machine learning classifiers, including random forest, support vector classifier, k-nearest neighbor, and decision trees. Hyperparameter tuning was performed for each model for over 486 data points. Regression predictive models were developed for both ink droplet velocity and volume with three linear models (linear, lasso, and ridge regression) and four non-linear models (random forest, decision tree, support vector regression, and k-nearest neighbor). Mean squared error and the coefficient of determination, r-squared value, were used to evaluate the performance of the predictive models. For the drop type classification models, k-fold of 5 yielded the highest accuracy for the RF, KNN, and DT models of around 98%. Similarly, for the regression based predictive models RF, DT and KNN accuracy results ranged from 97 to 99%. All the machine learning models were validated with experimental data with high prediction accuracies accuracy. This research serves as a foundation for developing design guidelines for 3D material inkjet printing with applications in biosensors, flexible electronics, and regenerative tissue engineering.

Build orientation optimization considering thermal distortion in additive manufacturing

Additive manufacturing (AM) technology enables the fabrication of three-dimensional objects with complex shapes and has been extensively applied in various industries. AM is a layer-wise fabrication process where a variety of factors affect manufacturing performance and product quality. One of the most important factor is the thermal distortion, which is caused by the high temperature gradients in the fabrication process. The thermal distortion is also influenced by the support structure of the printing object, and this distortion varies depending on the chosen build orientation. Additionally, for the overhang regions, extra supports are required for printing and will be removed in the post-processing. For the 3D printing process, the thermal distortion and support requirements are interconnected and linked to build orientation. To investigate suitable build orientation, the thermal distortion and support are quantified, and a multi-objective build orientation optimization method is proposed to obtain representative orientations. Based on the proposed method, 9 typical 3D shapes are evaluated. In addition, the single-objective build orientation optimization problem is studied and compared, and the influence of slicing layers per stage on the simulation accuracy and efficiency is discussed. The effectiveness and applicability of the method are verified, and representative directions can be obtained for different fabrication purposes.

3D printing of aligned silk fibroin microfibers covered with nitrogen-doped carbon dots for anti-counterfeiting

Counterfeiting has serious economic and social consequences, prompting researchers worldwide to develop innovative and highly secure anti-counterfeiting methods, including the use of various polymer printing techniques and the integration of functional materials to create patterns with customized designs that are easy to detect and cannot be falsified. Composite inks made of silk fibroin microfibers and polyvinyl alcohol or polyethylene glycol were developed to produce patterns with the microstructures aligned in the direction of extrusion during the 3D printing process. Fibroin microfibers were obtained via high temperature treatment and used as a precursor for carbon dot synthesis. This approach allowed the microfiber structure to be maintained and be amenable to the synthesis of carbon dots doped with N-heteroatom on its surface, resulting in a material that fluoresces bright blue when irradiated at 365 nm but remains invisible in normal lighting conditions

Effects of nozzle geometry and fiber orientation on the tensile strength of 3D printed continuous fiber reinforced composites

In material extrusion (MEX) based 3D printing, inter-filament voids are intrinsic to printing process. The void orientation, volume and shape are affected by multiple factors including the nozzle shape, stacking sequence and printing direction. In this study, the adverse effects of the inter-filament voids on tensile properties and damage modes were investigated numerically on 3D printed acrylonitrile butadiene styrene-carbon fiber (ABS/CF) continuous fiber-reinforced composites (CFRCs). Uniaxial tensile simulations were performed considering various nozzle geometries (circular, square), fiber orientations (θ=0o,30o,45o,60o,90o) relative to loading direction, and a regular stacking sequence of extrudates. The extrudate cross-section was modeled either using elliptical or superelliptical extrudates deposited from a circular or square nozzle, respectively. Excellent agreement was seen when the simulated results were benchmarked against several published experimental and numerical work. Simulated results showed that changing the nozzle shape from circular to square improved the mechanical properties across all fiber angles by lowering the void content by 7−8% and increasing the ultimate tensile strength (σT) by 11−18%, tensile stiffness (ET) by 6−8%, and the tensile failure strains (ϵT,fail) by 1−11%. For superelliptical extrudates the number of observed damage modes also reduced, and this is due to a 37.2% and 58.2% improvement in the inter-filament and inter-layer bond lengths, respectively. Also, when fiber angle became increasingly off-axis to tensile load direction, the strengths, moduli, and failure strains reduced for both circular and square nozzles. The significance of using microstructure geometries and explicitly modeling inter-filament voids for simulating MEX printed CFRCs was highlighted by comparing these results with both analytical calculations and simulated results of homogeneous solid CFRC blocks without voids.

Inkjet‐printed capacitively loaded Marchand baluns with enhanced fractional bandwidths

AbstractA comprehensive design methodology for the realization of ultra‐wideband and highly miniaturized capacitively loaded Marchand baluns (MBs), suitable for integration into balanced antenna systems is presented. A design technique to maximize the fractional bandwidth (FBW) for a given manufacturing process is demonstrated for the first time. Furthermore, a novel MB integration scheme using broadside‐coupled lines and 3D vertically integrated capacitors in a two‐material inkjet printing process is uniquely proposed. The concept has been experimentally validated within the S and C bands. It exhibits a footprint of 0.128 × 0.098 λg2, centre frequency of 4.4 GHz, and FBW of 121%. Its power loss, phase imbalance and amplitude imbalance were measured between 0.8 and 2.2 dB, 3 ± 2°, and 0.4 ± 0.4 dB, respectively.

A qualitative validation of an in-situ monitoring system for EHD inkjet printing via laser diffraction

Electrohydrodynamic inkjet printing enables high-resolution patterning for nano features. In-flight dynamics of EHD inkjet printing play an essential role in the quality control of printing results. We applied a laser diffraction/scattering in-situ analyzing setup for the EHD inkjet printing system to replace the zoom lens and high-speed camera imaging system. In contrast to conventional imaging systems, the laser diffraction/scattering system is based on analyzing the diffraction pattern and scattering intensity, respectively, which provided higher resolution for micro-scale jetting measurement and enabled sub-micron level jetting correlation between the voltage applied to the electrode and printing results. Furthermore, Taylor cone information from the nozzle head could also be analyzed in real-time to make adjustments to the printing process. In this work, we successfully validated the feasibility of laser diffraction analysis in-situ monitoring for EHD inkjet printing at micron and sub-micron levels.

Advancing 3-Dimensional Printed Burr Hole and Craniotomy Models for Neurosurgical Simulation Through Multimaterial Methods

Objective3D printing technology presents a promising avenue for the development of affordable neurosurgical simulation models, addressing many challenges related to the use of cadavers, animal models, and direct patient engagement. The aim of this study is to introduce and evaluate a new high-fidelity neurosurgical simulation model targeted for both burr hole and craniotomy procedures. MethodsTwelve different 3D printed skull models were manufactured using five different materials (PEEK, White Resin, Rigid10K, BoneSTN, SkullSTN) from three different 3D print processes (Fused Filament Fabrication, Stereolithography, Material Jetting). Six consultant neurosurgeons conducted burr holes and craniotomies on each sample while blinded to these manufacturing details. Participants completed a survey based on the qualities of the models, including; mechanical performance, visual appearance, interior feeling, exterior feeling, sound, overall quality, and recommendations for training purposes based on their prior experience completing these procedures on human skulls. ResultsThis study found that the multi-material stereolithography printed models consisting of White Resin for the outer table and Rigid 10K for the diploe and inner table were successful in replicating a human skull for burr hole and craniotomy simulation. This was followed by the porous General BoneSTN preset material on a Stratasys J750 Digital Anatomy Printer. ConclusionsThe findings indicate that widely accessible and economical desktop stereolithography 3D printers can provide an effective solution in neurosurgical training, thus promoting their integration in hospitals.

Contactless Printing of Food Micro-Particles Controlled by Ultrasound

Traditional food 3D printing technology, which primarily relies on mechanical control, often faces challenges such as low resolution, stringent material requirements, limited flexibility, and cumbersome equipment. To overcome these limitations, we developed an innovative food 3D printing method utilizing ultrasonic phased arrays and subsequently designed a contactless printing device employing this advanced technology. The device effectively manipulates the phase and amplitude of the ultrasonic phased array to focus the sound field on a specific location. We have successfully demonstrated preliminary control over the suspension of small food particles, including popcorn, chocolate, honey, and marshmallows, using this apparatus. Following this, popcorn particles were selected for detailed practical printing and further analysis.Additionally, we established a software platform, Ultr_printing, based on Ultraino, for intelligent simulation and control of the printing process. Leveraging FPGA and Arduino controllers, this setup facilitates rapid signal transmission from personal computers to sensors, ensuring quick and adaptable movement of suspended particles. Experimental results confirm that the device consistently controls the food particles, irrespective of their motion state. Notably, it achieves a printing precision of 0.16 mm when handling popcorn particles measuring 1.5 mm in diameter. The process, entirely contactless and free from contamination, preserves the original flavor of the food particles. This technology is particularly effective for high-precision printing of micro-scale shapes and offers considerable benefits in assembly and precision manufacturing.