Scaffolds are physical substrates for cell attachment, proliferation, and differentiation, ultimately leading to the regeneration of tissues. They must be designed according to specific biomechanical requirements, i.e., certain standards in terms of mechanical properties, surface characteristics, porosity, degradability, and biocompatibility. The optimal design of a scaffold for a specific tissue strongly depends on both materials and manufacturing processes, as well as surface treatment. Polymeric scaffolds reinforced with electro-active particles could play a key role in tissue engineering by modulating cell proliferation and differentiation. This paper investigates the use of an extrusion-based additive manufacturing system to produce poly(ε-caprolactone) (PCL)/pristine graphene scaffolds for bone tissue applications and the influence of chemical surface modification on their biological behaviour. Scaffolds with the same architecture but different concentrations of pristine graphene were evaluated from surface property and biological points of view. Results show that the addition of pristine graphene had a positive impact on cell viability and proliferation, and that surface modification leads to improved cell response.

Machine learning is reshaping gel-based additive manufacturing by enabling accelerated material design and predictive process optimization. This review provides a comprehensive overview of recent progress in applying machine learning across gel formulation development, printability prediction, and real-time process control. The integration of algorithms such as neural networks, random forests, and support vector machines allows accurate modeling of gel properties, including rheology, elasticity, swelling, and viscoelasticity, from compositional and processing data. Advances in data-driven formulation and closed-loop robotics are moving gel printing from trial and error toward autonomous and efficient material discovery. Despite these advances, challenges remain regarding data sparsity, model robustness, and integration with commercial printing systems. The review results highlight the value of open-source datasets, standardized protocols, and robust validation practices to ensure reproducibility and reliability in both research and clinical environments. Looking ahead, combining multimodal sensing, generative design, and automated experimentation will further accelerate discoveries and enable new possibilities in tissue engineering, biomedical devices, soft robotics, and sustainable materials manufacturing.