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End-effector tools wear prediction: analysis and modelling of a CNC machine and new formulation of the friction coefficient

Emanuele Pansica

End-effector tools wear prediction: analysis and modelling of a CNC machine and new formulation of the friction coefficient.

Rel. Alessandro Rizzo, Giovanni Guida. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022

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In recent years, we have been witnessing a technological transformation of industry, the so-called Industry 4.0. In this context, the State of Health assessment (SOH) of industrial machines is a very important topic because by having information about the SOH of the machines, it is possible to operate preventive maintenace to avoid damages that may occur and, consequently, causing waste of money and time. Implementing SOH, companies could have more efficient production systems and reduce costs related to maintenance and machine downtime. The MOREPRO Project, owned by Brain Technologies, aims to develop a system capable of estimating the SOH of the end effector tools of a CNC machine in real-time. In particular, their main purpose is to develop a technique that is first able to estimate the friction coefficient to which the tool is subjected during metal cutting operations, and then extract from this parameter the information necessary to estimate the SOH of the tool at the end of the stroke. This thesis work aims to develop a similar Finite Element Method (FEM) analysis using the Mathworks development environment to obtain a model that can describe the interaction of the tool with the workpiece, and then find a new formulation of the coefficient of friction. Furthermore, from this model, artificial data can be obtained andused to develop estimation methodologies. To achieve this, firstly, I created the model of the CNC machine and then developed the trajectories that the end effector follows in the operational space. Thanks to the identification of the points of interaction between the end effector and the workpiece, mainly developed by my colleague, who provided me with necessary data for doing final simulations, I developed a dynamic formulation of the metal cutting operation to obtain an estimate of the value of the friction coefficient. The main activities carried out in this thesis project can be divided as follows: 1.??Creation of a CNC machine model: the CNC machine analysed can be modelled as a robot consisting of two parts. The first part aims to move and position the cutting tool while the second part aims to position and orientate the workpiece. The models of the two parts were created on MATLAB and SIMULINK using the knowledge acquired in the Robotics course. These models are necessary to obtain the 3D coordinates of the tool and the workpiece. 2.??Creation of trajectories in the operating space: to simulate the movement made by a CNC machine, it was necessary to give as input a trajectory to be followed by our model. However, for our objective, we were interested in the movements of the end-effector tool; thus, trajectories were developed in operational space. A segment in the space trajectory and a circumference in the space trajectory were developed. 3.??Dynamic model of a CNC machine and estimation of the coefficient of friction: Today, the cutting process remains a very difficult one to model dynamically, however it was possible from cutting theory and empirical formulations to estimate the forces occurred during the cutting process, in particular, the normal and shear forces. Once obtained these forces, it was possible to come up with a formulation for the friction coefficient, which depends on the cutting parameters, the type of material used in the machining process and the geometry.

Relators: Alessandro Rizzo, Giovanni Guida
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 115
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: Brain technologies
URI: http://webthesis.biblio.polito.it/id/eprint/22669
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