Smart Bioinks for the Printing of Human Tissue Models

3D bioprinting has tremendous potential to revolutionize the field of regenerative medicine by automating the process of tissue engineering. A significant number of new and advanced bioprinting technologies have been developed in recent years, enabling the generation of increasingly accurate models of human tissues both in the healthy and diseased state. Accordingly, this technology has generated a demand for smart bioinks that can enable the rapid and efficient generation of human bioprinted tissues that accurately recapitulate the properties of the same tissue found in vivo. Here, we define smart bioinks as those that provide controlled release of factors in response to stimuli or combine multiple materials to yield novel properties for the bioprinting of human tissues. This perspective piece reviews the existing literature and examines the potential for the incorporation of micro and nanotechnologies into bioinks to enhance their properties. It also discusses avenues for future work in this cutting-edge field.

Prediction and Compensation of Color Deviation by Response Surface Methodology for PolyJet 3D Printing

PolyJet 3D printing can produce any color by mixing multiple materials. However, there are often large deviations between the measured color of printed samples and the target color (when the target color is used as the specified color in the printer software). Therefore, to achieve a target color on a printed sample, the specified color in the printer software should not be the same as the target color. This study applies response surface methodology (RSM) to determine the optimal color specification to compensate for color deviations of the measured color of printed samples from the target color in PolyJet 3D printing. The RSM has three steps. First, a set of experiments are designed for a target color according to central composite design. Second, the experimental data are used to develop a second-order multivariate multiple regression model to predict the deviation between the measured color and the target color. Third, the optimal color specification (often different from the target color) is determined by using the developed predictive model and the desirability function. When the optimal color specification is used as the specified color in the printer software, the deviation between the predicted color of the printed sample and the target color is minimized. The proposed method is applied to four target colors to demonstrate its effectiveness. The results show that the proposed method performs better than the conventional color specification method without compensation in achieving the four target colors by 33% on average.

The impact of thermal reprocessing of 3D printable polymers on their mechanical performance and airborne pollutant profiles

The alterations in volatile organic compound (VOC) and ultrafine particulate (UFP) matter emission profiles following thermal reprocessing of multiple materials were examined. Additionally, mechanical performance of the materials was studied. The VOCs were identified by collecting air samples with Tenax® TA tubes and analyzing them with a GC–MS system. UFP concentrations were monitored with a portable ultrafine particle counter. Total VOC emissions of all materials were reduced by 28–68% after 5 thermal cycles (TCs). However, slight accumulation of 1,4-dioxane was observed with poly(lactic acid) materials. UFP emissions were reduced by 45–88% for 3D printing grade materials over 5 TCs but increased by 62% in the case of a waste plastic material over 3 TCs. The mechanical performance of the materials was investigated by measuring their tensile strengths (TSs) and elastic moduli (EM) with an axial-torsion testing system. The reprocessed materials expressed fluctuations in their 3D printing qualities and mechanical performances. The mechanical performances were observed to reduce only slightly after 5 TCs, and the trend was observable only after the data was mass-normalized. The TSs of the samples were reduced by 10–24%, while the EM were reduced by 1–9% after 5 TCs. The TS and EM of one material were increased by 14 and 33%, respectively. In conclusion, recycled polymers are plausible 3D printing feedstock alternatives as they possess acceptable mechanical performance and low emittance according to this study. Furthermore, non-3D printing grade polymers may be applied in a 3D printer with caution.

SIMPLE EVOLUTIONARY STRUCURAL OPTIMIZATION FOR MULTIPLE MATERIALS

An evolutionary procedure for multiple materials is presented. A material is selected due to an allowable stress interval for the material. The presented method includes a mesh-independent filter. The proposed algorithm is applied to three examples with four or five materials. In the presented examples, the effect of an evolution rate, a filter setting, and the number of elements, are shown in a simplified way. It is shown that the final topology of structure meets the stress requirements of the materials.

Low Reynolds Number Swimming Near Interfaces in Multi-Fluid Media

Microorganisms often swim within heterogeneous fluid media composed of multiple materials with very different properties. The swimming speed is greatly affected by the composition and rheology of the fluidic environment. In addition, biological locomotions are also strongly influenced by the presence of phase boundaries and free interfaces, across which physical properties of the fluid media may vary significantly. Using a two-fluid immersed boundary method, we investigate the classical Taylor’s swimming sheet problem near interfaces within multi-fluid media. The accuracy of the methodology is illustrated through comparisons with analytical solutions. Our simulation results indicate that the interface dynamics and phase separation in the multi-fluid mixture are closely coupled with the movement of the swimmer. Depending on the interface location, the frictional coefficient, and the multi-fluid composition, the swimmer can move faster or slower than that in a single phase fluid.

Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors (IDETC2021-71570)

Scientific and engineering problems often require the use of artificial intelligence to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners, they have difficulties in accommodating big datasets, qualitative inputs, and multi-type responses obtained from different simulators, which has become a common challenge for data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. The method is built upon the latent variable Gaussian process (LVGP) model where qualitative factors are mapped into a continuous latent space to enable GP modeling of mixed-variable datasets. By extending variational inference to LVGP models, the large training dataset is replaced by a small set of inducing points to address the scalability issue. Output response vectors are represented by a linear combination of independent latent functions, forming a flexible kernel structure to handle multi-type responses. Comparative studies demonstrate that the proposed method scales well for large datasets, while outperforming state-of-the-art machine learning methods without requiring much hyperparameter tuning. In addition, an interpretable latent space is obtained to draw insights into the effect of qualitative factors, such as those associated with “building blocks” of architectures and element choices in metamaterial and materials design. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism with aperiodic microstructures and multiple materials.

Vibration Characteristics of 3D Printed Viscoelastic Graded Polymeric Plates

The exploration of structures made of multiple materials is a growing area of research as additive manufacturing processing technologies such as fused deposition modeling (FDM) 3D printing allows for their fabrication. Such a technology allows for rapid prototyping of structural components with complex geometries or spatially distributed materials with different properties and functionalities. By selecting suitable spatial distribution of materials, the performance of structural components can be manipulated and enhanced as per different engineering application needs. For a low-cost design of structural prototypes using 3D printing processes, viscoelastic polymeric materials are often used, having inherent damping properties. In this research, vibration characteristics of thin plates which are axially graded with multiple polymeric materials are investigated. The goal is to understand the influence of material grading on the frequency and damping characteristics of graded plates. Although in literature, material grading along the thickness in designing composite laminates and their vibration characteristics are available, the performance of plates having axially graded viscoelastic polymers have not been investigated yet. Through systematic modeling and experimental plans, vibration characteristics of axially graded viscoelastic plates are presented here. In particular, the damping performance for different grading schemes is evaluated. It is anticipated that such analysis will allow accurate modeling and testing of design prototypes of structural components for future research, such as design and testing of graded panels for enhanced flutter characteristics.