(c) PHM-IC 2026
Juan Vanerio and fellow researchers from the AIT Austrian Institute of Technology's Center for Digital Safety & Security, together with industrial partner FACC AG, have received the Best Paper Award at the 2026 Prognostics and System Health Management Conference (PHM-IC). The award-winning paper, “Physics-Informed Graph-Based Modeling for Predictive Monitoring and Optimization of Industrial Autoclave Curing,” was presented at the 16th PHM-IC conference, held in Marseille, France, from May 26 to 29, 2026.
The paper was authored by AIT researchers Juan Vanerio, Laura Bernadó, Anahid Wachsenegger, Michela Vignoli, and Pedro Casas, in collaboration with Santhosh Ayalur-Karunakaran from FACC. The research focuses on the modeling and optimization of industrial autoclave curing processes, which are essential for manufacturing carbon-fiber-reinforced polymer (CFRP) components used in aerospace applications.
Using real industrial data from FACC, a leading aerospace company specializing in lightweight aircraft structures, the research team developed a physics-informed, graph-based digital shadow to model and predict thermal behavior during autoclave curing. The proposed Physics-guided Constraint-based Graph Learning (PCGL) approach combines data-driven modeling with physical constraints to capture thermal interactions and predict the thermal evolution of an autoclave load. The goal is to optimize component placement, shorten curing times, and reduce energy consumption, while ensuring proper curing conditions. The approach also supports improved product quality, reduced manufacturing defects and scrap, and greater production efficiency.
The research was conducted within the FFG-funded AI4SimProd project, coordinated by AIT in collaboration with FACC and the LKR Light Metal Competence Center Ranshofen. The project develops AI-assisted digital twin technologies for energy- and resource-efficient industrial manufacturing, contributing to AIT's broader goal of supporting the sustainable transformation of industry through advanced modeling and optimization of complex production processes.
This international award highlights the potential of innovative AI technologies for sustainable and efficient manufacturing processes.
We offer our warmest congratulations!