Dario Caniglia
End-effector tools wear prediction: interaction with the workpiece modelling in a quasi-FEM approach.
Rel. Alessandro Rizzo, Giovanni Guida. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021
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Abstract
Nowadays, in the “Industry 4.0” context, an important topic is the state of health (SoH) estimation of machines because, knowing the SoH of machines, it is possible to intervene before that some damage could occur. Thus, the companies can increase the performance of their production systems and to reduce the maintenance costs. The MorePRO project, owned by Brain Technologies, fits in this context. This project has the goal of developing a real-time system able to estimate the SoH of the end-effector tools in a CNC machine. The technique that Brain Technologies wants to develop is able to estimate the friction coefficient to which the tool is exposed during metal cutting operations, extracting from this parameter the information to estimate the SoH of the end-effector tool.
This thesis goal is to start the development of a similar FEM (Finite Element Method) analysis in Mathworks environment to obtain a model of the tool interaction with the work piece
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