Bioprinting faces several barriers to clinical translation, including difficult scaffold handling, contamination risk, the need for bioreactor maturation, and poor matching between printed constructs and defect geometry. In situ bioprinting addresses these limitations by directly depositing biomaterials onto the patient, following the anatomical defect while using the body as a natural bioreactor to enhance tissue maturation. Among current solutions, robotic manipulators are particularly promising because they reduce human intervention and improve precision, enabling the regeneration of complex defects.

To investigate this technology, we developed and validated IMAGObot, a 5-axis open-source robotic arm re-engineered for in situ bioprinting. Following a standardized workflow, the platform performs four key steps: (i) acquisition of the defect geometry, (ii) path planning, (iii) path registration, and (iv) in situ bioprinting. A touch probe acquires the defect geometry and assesses tissue mechanical properties. An in-house MATLAB® algorithm generates non-planar trajectories for complex geometries, while registration is achieved using anatomical landmarks or fiducial markers. Bioprinting can then be performed with multiple technologies, including pneumatic extrusion, valve-jet, and inkjet printing, enabled by an automatic tool-changing system for multimaterial and multiscale fabrication.

To improve construct stability in physiological conditions, IMAGObot integrates a photo-crosslinking device with adjustable LED positioning. LEDs opposite the printing direction selectively irradiate the deposited filament, minimizing needle exposure and preventing clogging.

The platform was successfully validated on anthropomorphic phantoms, demonstrating the potential of robotic in situ bioprinting for tissue engineering applications, particularly in accessible tissues such as skin, bone, and cartilage.

All the details about IMAGObot open-source project and a standalone Matlab® app for management of the operating workflow are available at https://github.com/CentroEPiaggio/IMAGObot.

.

In 2013 Dr. Skylar Tibbits introduced the term “4D printing” to denote the fabrication via 3D printing of structure able to shape-transform overtime when exposed to an external stimulus, such as heat, pH, and light. 4D printing relies on the patterning in space of smart materials, namely materials that undergo to useful, predictive, reproducible, and macroscopic physical/chemical changes in their properties as a consequence of an environmental stimuli. Impactfully, 4D printed structures achieve their functions through this pre-determined property change, that usually display macroscopically as a shape changing, thus possessing several advantages over traditional static structures and funding application in multiple fields of biomedical interest, such as tissue engineering, drug delivery systems and medical devices.

Our goal is the expansion and consolidation of 4D printing as an enabling manufacturing technology to program the environmental-triggered physical evolution of structures of biomedical interest. With this aim, in the recent years, we fabricated by 4D printing several devices of biomedical interest, such as scaffolds for trachea engineering and neural tube modeling and medical devices to perform intestinal anastomosis and for the treatment of the short bowel syndrome. These studies were conducting in collaboration with bio-materialists, biologists, and physicians from all over the world. Our approach in the design and fabrication of 4D (bio)printed devices is systematic and relies on the use of mathematical models, both analytical and computational, to investigate the desired property modification/shape morphing of the structures according to the material properties and their patterning in the 3D space. Moreover, we focus on the characterization and optimization of smart materials, with the aim to make the 4D printable.

Concurrently, we are also focusing on the formalization of the 4D printing approach, by means of systematic methodologies for engineering design and artificial intelligent.