The effect of light scattering is non-negligible in micro-scale DMD-based 3D printing. Light scattering results in unwanted exposure and polymerization, thus deteriorating the fabrication resolution and fidelity. We report using machine learning (ML) approach to mitigate the effect of light scattering. A neural network (NN) was designed to study the highly-sophisticated relationship between the input digital masks and their corresponding output 3D printed structures. After trained with 300 pairs of digital masks and printed structures, the neural network was able to optimize the 3D printing process by suggesting the optimal grayscale digital masks which are not necessarily identical to the desired structures. Verification results showed that using NN-generated digital masks yielded significant improvements in printing resolution and fidelity compared to using masks that are identical to the desired structures.
DMD-based 3D printing is a powerful tool for making high-resolution biomimetic functional tissues and organs with various biomaterials for tissue engineering and regenerative medicine. A plethora of tissues have been fabricated using this technology including liver, heart, lung, kidney, blood vessels, cartilage, and placenta. In this article, we show prevascularization of the artificial tissue constructs using DMD-based 3D printing, which is essential to maintain the long term viability and function of a thick tissue. We also show a 3D printed biomimetic hepatic model that recapitulates the microarchitecture as well as the heterogeneous cell population of various cell types in the native liver tissue. It is important for the biomaterials to mimic the native microenvironment. Finally, we demonstrate that 3D printed tissue-specific decellularized extracellular matrix can improve cell response and behavior.
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