Journal of Manufacturing Science and Engineering-transactions of The Asme最新文献

Nested printing, a special type of embedded printing, enables the fabrication of multilayered enclosed structures, in particular those resembling biological organs. As with any printing process, shape fidelity in nested printing is of great importance to dimensional accuracy, structural integrity, and/or functionality of 3D-printed parts. Particularly, the shape fidelity may be compromised due to the upflow and the additional volume introduced by internal depositions, and the latter is not well studied yet and calls for robust mitigation approaches. This study aims to establish a B-spline function-based three-dimensional (3D) freeform compensation method to offset the effect of internally deposited volumes in internally nested structures during nested printing. Particularly, printed nested structures are visualized using image-based segmentation and reconstruction, shape fidelity is assessed by measuring deviations between reconstructed and designed models using 3D structural similarity analysis, and a distortion field and a corresponding compensation field are approximated using a B-spline function-based method, resulting in a compensated 3D model for final nested printing. This compensation method reduces the mean printing error from 9.35% to 2.02% for the first enclosing layer and from 17.59% to 0.47% for the second enclosing layer, respectively, for a canonical nested structure. Further, the demonstration case of a 3D brain limbic system model shows a reduced mean printing error from 10.67% to 1.40% for the enclosing white matter region. The compensation-based mitigation strategy using the B-spline function effectively enhances shape fidelity during nested printing.

The field of tissue engineering has significantly advanced with the development of extrusion-based bioprinting. This technique utilizes shear forces to generate filaments for fabricating intricate structures. The printability and structural integrity of bioprinted constructs rely heavily on the rheological properties of bioinks, particularly viscosity, which varies with the shear rate for non-Newtonian materials. Since the shear rate at the nozzle tip fluctuates during extrusion, it is essential to understand how bioink composition influences this behavior. This study investigates the rheological behavior of ALGEC bioinks, a novel formulation composed of ALginate, GElatin, and 2,2,6,6-Tetramethylpiperidine 1-oxyl (TEMPO)-oxidized nanofibrillated cellulose (TO-NFC). The bioinks were prepared with varying concentrations: alginate (0-5.25%), gelatin (0-5.25%), and TO-NFC (0-1.5%), with a maximum total solid content of 8%. Viscosity was conducted over shear rates ranging from 0.1 to 100 s^-1, with 252 viscosity data points used 80% for training and 20% for validation. To predict viscosity, polynomial fit and interaction-based multiple regression models were developed. Experimental data were used to estimate viscosity based on bioink composition and shear rate, with the best-performing model achieving an R² of 0.98 and an mean absolute error (MAE) of 0.12. These predictive models were further utilized to optimize ALGEC formulations to achieve targeted viscosity ranges. Constructs were bioprinted using a random and an optimized composition, demonstrating the effectiveness of model-driven bioink optimization. These findings enhance tissue engineering by improving bioink printability, leading to structurally stable bioprinted constructs for regenerative medicine applications.

Among various 3D bioprinting methods, extrusion-based bioprinting stands out for its ability to maintain high cell viability and create intricate scaffold structures. However, working with synthetic polymers or natural shear-thinning hydrogels requires precise control of rheological properties, such as viscosity, to ensure scaffold stability while supporting living cells. Traditionally, researchers address these challenges through extensive experimentation, separately optimizing material properties and bioprinting performance. This process, though effective, is often slow and resource-heavy. To streamline this workflow, computational approaches like machine learning are proving invaluable. In this study, a decision tree model was developed to predict the viscosity of bioinks across various compositions with high accuracy, significantly reducing the trial-and-error phase of experimentation. Once viscosity is optimized, k-means clustering is applied to analyze and group scaffolds based on their mechanical and biological properties. This clustering technique identifies the optimal characteristics for scaffolds, balancing structural fidelity and cell viability. The integration of these computational tools allows researchers to optimize bioink formulations and printing parameters more efficiently. By reducing experimental workload and improving precision, this approach not only accelerates the bioprinting process but also ensures that the resulting scaffolds meet the required mechanical integrity and provide a conducive environment for cell growth. This study represents a significant step forward in tissue engineering, offering a robust, data-driven pathway to enhance both the efficiency and quality of 3D bioprinted constructs.

The field of 3D bioprinting is rapidly expanding as researchers strive to create functional tissues for medical and pharmaceutical purposes. The ability to print multiple materials, each containing various living cells, brings us closer to achieving tissue regeneration. Deliberately transitioning between different material types encapsulating distinct cells and extruding through a single outlet, can lead to the achievement of user-defined material distribution, which is still challenging. In a previous study, we designed a Y-shaped nozzle connector system that allowed for continuous deposition of multiple materials through a single outlet. This system was made of plastic and had a fixed switching angle, rendering it suitable for a single use. In this article, we present the updated version of our nozzle system, which includes a range of angles (30 deg, 45 deg, 60 deg, and 90 deg) between the two materials. Changing the angles helps us figure out how that affects the control of backflow and minimizes the overall material switching time in the nozzle. We used stainless steel as the fabrication material and recorded the overall material switching time, comparing the effects of the various angles. Our previously developed hybrid hydrogel, which comprised 4% alginate and 4% carboxymethyl cellulose (CMC), was used as a test material to flow through the nozzle system. The in-house fabricated nozzle connectors are reusable, sterile, and easy to clean, ensuring a smooth material transition and flow. Our proposition can offer to achieve user-defined material distribution across a given region with appropriate selection of rheology and printing process parameters.