The development of advanced monitoring tools is emerging as a key enabler for scaling up additive manufacturing in industrial settings. In polymer powder bed fusion (PBF) for 3D printing, most existing systems focus only on surface observation, while crucial processes take place within the material. The cooling phase, in particular, plays a decisive role: even slight differences in cooling rates within the same production cycle can lead to variations in microstructure and, ultimately, inconsistent mechanical properties.
The uTEAM project tackles this limitation with a novel microwave-based monitoring approach. Unlike conventional thermal imaging, microwaves can penetrate materials, enabling temperature measurements inside components during and after printing. This allows the reconstruction of thermal tomographic maps and a full volumetric view of temperature distribution across the build chamber, including during the most critical stages of the process.
“Microwaves provide access to previously invisible data, opening new possibilities for more controlled and reliable manufacturing processes,” says Samuel Poretti, head of the scientific area Analog and Radio Frequency Electronics, Telecom and Imaging Systems at ISEA. “Applications range from advanced quality control to real-time process optimisation, as well as waste reduction and the development of machine learning-based predictive models. The impact is particularly relevant for high-precision sectors such as aerospace, automotive and medical.”
Access to internal temperature profiles also opens up new possibilities for directly improving the production process. For instance, it enables the optimisation of cooling “temperature ramps”, the adjustment of laser parameters based on part positioning, and the development of pre-processing algorithms to enhance part arrangement within the build chamber. These data are valuable not only for machine manufacturers but also for end users, as they support the creation of increasingly accurate predictive models of thermal behaviour, including approaches based on machine learning. Such models can significantly improve production quality and repeatability. In the longer term, these systems could enable fully automated printing processes, with real-time adjustments, greater uniformity across components, and a substantial reduction in variability of mechanical properties.
The Eurostars uTEAM project brings together partners with complementary expertise: the Institute of Systems and Electronics Applied (ISEA) at SUPSI, responsible for developing microwave signal measurement technologies; the University of Genoa (UNIGE), which designed the algorithms for reconstructing thermal maps; INSPIRE AG, in charge of analysing polymer behaviour and developing high-performance material blends with tailored mechanical and electrical properties; FOS Italia, project coordinator and responsible for system integration; and Schleiss RTPech (St. Gallen), which provided the printers, mechanical support and access to experimental testing.
Through this collaboration, uTEAM marks a significant step forward in the development of next-generation monitoring tools for additive manufacturing, contributing to improved reliability, quality and long-term sustainability in polymer powder bed fusion processes.