Antonia Verde
End-effector tools wear prediction: machine and interaction modeling, system identification based on the EKF approach.
Rel. Alessandro Rizzo, Giovanni Guida. Politecnico di Torino, Master of science program in Mechatronic Engineering, 2021
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Abstract
This thesis work is part of the project MOREPRO, an industrial program owned by BrainTechnologies, whose main goal is the realisation of a predictive monitoring system for the tool’s wear and for the state of health of the machinery in real-time. The whole project is carried out in teamwork; in particular, the team’s partition is the following: •modelling team •prediction team •requirements team. My initial role was within the modelling team with the aim of finding a kinematic and dynamic model of the system and create a simulation environment for the robot considered. Afterwards, I continued the modelling work for the prediction team in order to find a model for the interaction between the end-effector of the machine and the workpiece.
The crucial parameter considered in the interaction model is the friction coefficient because it has a strong impact on the tool’s wear
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