Networked control and supervision system based on Artificial Intelligence techniques for optimizing high speed machining processes (COREMAV)
National Research Project.
This project aims at the design and implementation of networked control and supervision system (NCSS) based on Artificial Intelligence techniques for optimizing high-speed machining processes. The main idea is to capitalize synergies between information and knowledge from sensorial information and expert operators, between classic control strategies (e.g., internal model control, feedback linearization) and Artificial Intelligence (hybridization of knowledge representation based on Fuzzy Logic, learning on the basis of Artificial Neural Networks and tuning based on genetic algorithms). The synergy between advanced computational algorithms in standards middleware and communication technologies is another essential issue to be considered.
The method for designing and implementing NCSS, derived for a first approach of an Interconnection theory that will be addressed in this project, will be applied to optimize high-speed machining processes through a networked control and supervision system. High-speed milling process is the choice among all machining processes due to its nonlinear and time variant behavior, being an excellent recipient of the developed strategies. Moreover, the high-speed milling process is a key process for important industrial sectors such as mould and die, automotive, aeronautic, machine tool, and others.
From technological viewpoint two key issues will be addressed. One subject will be networked control of main variables including internal CNC variables and measured variables (e.g., torque, vibration) based on AI techniques, allowing optimal exploitation of machine tools through higher material removal rates and taking into account constraints. Another issue will be networked supervision of high-speed cutting process (i.e., detection of vibrations) and cutting tool (i.e., tool wear).