Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G’) and loss (G”) moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G’ and G”. We employed a multilayer perceptron (MLP) and successfully predicted G’ and G” from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R² value to be 0.89. Next, we adopted two generative DL models named variational autoencoder (VAE) and conditional variational autoencoder (CVAE) to learn data patterns and generate constituent compositions. With these generative models, we produced synthetic data with the same statistical distribution as the real data of actual hydrogel fabrication, which was then validated using Student’s t-test and an autoencoder (AE) anomaly detector. We found that none of the seven generated gel constituents were significantly different from the real data. Our trained DL models were successful in mapping the input–output relationship for the 3D-printed hydrogel substrates, which can predict multiple variables from a handful of input variables and vice versa.

Stereolithography (SLA) is a popular additive manufacturing (AM) method frequently used in research and various industrial sectors. The acrylate resin used in this research is renowned for its flexibility and durability, enabling the creation of flawless 3D-printed parts with exceptional mechanical properties. This study aims to enhance the thermomechanical properties of 3D-printed hollow glass microballoon (HGM)-filled composite materials by adding minimal HGM into the acrylate resin. We investigated the material properties through uniaxial compression tests, dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM). To validate the results, a numerical investigation and a machine learning (ML) approach were carried out and compared with the experimental results. Adding a small number of microballoons increases compressive strength and stiffness. The viscoelastic behavior of the samples also provides an estimate of resilience at higher temperatures, considering the addition of filler material into the resin. Our study shows that the addition of 0.04% of HGM increased compressive strength by around 99.30% compared to the neat sample, while the stiffness increased by around 31.42% compared to the neat sample at 0.05% of HGM. It can also be estimated that the suitable range of HGM addition for the resin we used exists between 0.04% and 0.05%, where the materials achieve their maximum strength and stiffness. In addition, a predictive machine learning (ML) model, namely Random Forest Regressor (RFR), shows low mean squared error (MSE), mean absolute error (MAE), and excellent R² scores, demonstrating the goodness of the model’s performance. This modern approach can guide us to selecting a suitable filler percentage for the photopolymer resin for 3D printing and making it applicable to different engineering prospects.