Smart manufacturing has been in the media for a long time, but the reality shows that traditional mechanical manufacturing industries have not been able to implement data solutions aligned with Industry 4.0 standards. This work inquiries into the possibility of measuring cutting tool vibrations for CNC turning machines and presents the data analysis and a predictive model to identify tool wearing that can affects integrity surface quality of the manufactured component. These preliminary results are orientated towards implementing a predictive maintenance methodology in cutting tools.
In recent years, the emerging technologies in the context of Industry 4.0 have led to novel approaches in process monitoring and control, such as the introduction of Reinforcement Learning and Digital Twins. Consequently, large amounts of data, precise modelling and exhaustive simulations are required. The aim of this work is to propose a methodology based on the technique of backward selection to reduce the number of reference points in the simulation stage of manufacturing processes, enhancing the efficiency of data generation and the simplicity of the simulations. The methodology is proved in the particular case of plastic injection moulding simulations.
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