The ASTREA project aims to develop monitoring systems for process parameters in large-scale additive manufacturing (AM), implement communication protocols, and perform thermal evaluation of layer adhesion in prototypes and large, complex parts produced by AM. In addition, the project seeks to update the portfolio of raw materials available for this type of system by conducting composition studies and laboratory-based characterization (thermal, mechanical, rheological, among others), operating extrusion equipment, and optimizing prototypes and parts produced through AM using materials based on recycled polymers.

The new material formulations to be produced will be inherently recyclable and obtained from waste of natural or fossil origin (thermoplastics) and will be used for the production of components for the furniture industry. Monitoring and control of robotic arms are fundamental to increasing efficiency and promoting innovation in manufacturing processes. It is essential to implement solutions that: (a) perform real-time monitoring, collecting data from sensors; (b) detect anomalies; and (c) dynamically adjust possible operational parameters. The application of AI (Machine Learning) algorithms, when fed with a significant volume of data, becomes extremely valuable for both failure prediction and predictive and proactive equipment maintenance.

The main objective of the ASTREA project is to improve manufacturing accuracy and the quality of manufactured products, increase operational efficiency, reduce failures and/or downtime, and avoid material waste.