![\"\"](\"https:\/\/www.aitem.org\/wp-content\/uploads\/2025\/06\/OPTIMAL_Project.png\")
<\/p>","protected":false},"excerpt":{"rendered":"At the University of Parma, the Department of Engineering for Industrial Systems and Technologies leads cutting-edge research in the optimization of manufacturing processes through numerical simulation and Machine Learning. Within the GIGAGREEN project, our studies on laser texturing aim to enhance the adhesion and service life of lithium-ion battery electrodes, boosting both efficiency and sustainability. In OPTIMAL, we are developing artificial neural-network (ANN) based optimization algorithms for laser lithography: a neural network models the physical process, correcting elevation errors and proximity effects in maskless greyscale laser lithography based on the incident laser energy. We also combine FEM and ANN approaches to optimize plastic deformation processes such as extrusion, reducing waste and costs while improving the properties of the components.<\/p>\n
<\/p>","protected":false},"excerpt":{"rendered":"
At the University of Parma, the Department of Engineering for Industrial Systems and Technologies leads cutting-edge research in the optimization of manufacturing processes through numerical simulation and Machine Learning. Within the GIGAGREEN project, our studies on laser texturing aim to enhance the adhesion and service life of lithium-ion battery electrodes, boosting both efficiency and sustainability. In OPTIMAL, we are developing artificial neural-network (ANN) based optimization algorithms for laser lithography: a neural network models the physical process, correcting elevation errors and proximity effects in maskless greyscale laser lithography based on the incident laser energy. We also combine FEM and ANN approaches to optimize plastic deformation processes such as extrusion, reducing waste and costs while improving the properties of the components.<\/p>\n