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, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) | Preview |
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
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
