ASTREA – Advanced Sensors and Technologies for Robotic Extrusion and Automation

The ASTREA project aims to develop monitoring systems for the parameters of large-scale additive manufacturing (AM) processes, implementation of communication protocols, and thermal evaluation of layer adhesion in prototypes and large complex parts produced by AM.

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In addition, this project intends to update the portfolio of raw materials available for these systems by conducting studies on composition and characterization (thermal, mechanical, rheological, among others) in a laboratory environment, operating extrusion equipment, and optimizing prototypes and parts using AM from recycled polymer-based materials. The new material formulations to be produced will be intrinsically recyclable and derived from natural or fossil (thermoplastic) waste, and they will be used for manufacturing components for the furniture industry.

Monitoring and controlling 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 by collecting sensor data, (b) detect anomalies, and (c) dynamically adjust possible operational parameters. The application of AI (Machine Learning) algorithms, when supported by large  volume of data, becomes extremely valuable for both failure prediction and predictive and proactive maintenance of equipment.

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

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DONE Lab (UMinho) will play a fundamental role in the research, development, and transfer of knowledge and base technologies, as well as in their integration into new products and processes, in the areas of polymer engineering (materials, processing tools, characterization, and product and process development), conception and production of prototypes and demonstrators by AM in an initial testing phase, computer-aided engineering (CAE) studies – process simulation, structural simulation, and development of solutions for large-scale AM.