The augmented demand for medical devices devoted to tissue regeneration and possessing a controlled micro-architecture means there is a need for industrial scale-up in the production of hydrogels. A new 3D printing technique was applied to the automation of a freeze-gelation method for the preparation of chitosan scaffolds with controlled porosity. For this aim, a dedicated 3D printer was built in-house: a preliminary effort has been necessary to explore the printing parameter space to optimize the printing results in terms of geometry, tolerances and mechanical properties of the product. Analysed parameters included viscosity of the starting chitosan solution, which was measured with a Brookfield viscometer, and temperature of deposition, which was determined by filming the process with a cryocooled sensor thermal camera. Optimized parameters were applied to the production of scaffolds from solutions of chitosan alone or with the addition of raffinose as a viscosity modifier. Resulting hydrogels were characterized in terms of morphology and porosity. In vitro cell culture studies comparing 3D printed scaffolds with their homologous produced by solution casting evidenced an improvement in biocompatibility deriving from the production technique as well as from the solid state modification of chitosan stemming from the addition of the viscosity modifier.

3D biomaterial manufacturing strategies show an extraordinary driving force for the development of innovative therapies in the tissue engineering field. Here, the behaviour of 3D printed chitosan (CH)-based scaffolds was explored as a function of the post-printing gelation process. To this purpose, gel forming properties of different media were tested on their capability to retain 3D structure, water content, mechanical resistance and surface/internal porosity. Three different gelation media (i.e. KOH 1.5 M, Na₂CO₃ 1.5 M, ammonia vapours) were selected and the 3D CH scaffolds were tested in terms of biocompatibility toward fibroblast as skin associated human cell line.

Online process control is a crucial task in modern production systems that use digital twin technology. The data acquisition from machines must provide reliable and on-the-fly data, reflecting the exact status of the ongoing process. This work presents an architecture to acquire data for an Additive Manufacturing (3D printer) process, using a set of consolidated Internet of Things (IoT) technologies to collect, verify and store these data in a trustful and secure way. The need for online monitoring and fault detection is addressed by the development of a classifier using Convolutional Neural Networks. This deep learning approach, using temporally aligned vibration data provided by the underlying architecture, allows raw data processing to detect patterns without signal pre-processing and without domain-specific knowledge for model building.