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End-effector wear monitoring system of milling machine 5-axis: System Design and Validation

Adriano Marzio

End-effector wear monitoring system of milling machine 5-axis: System Design and Validation.

Rel. Stefano Carabelli, Giovanni Guida. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021


Nowadays, the automation of industrial production is an area of particular interest for the optimization of production lines. Industrial companies invest a lot of resources in research focused on improving production line control technologies, the optimization of control strategies lead to a clear reduction in risks and processing costs. In industrial production, mechanical processing machines have high precision CNC controls, moreover sensors and line monitoring activities are increasingly performing. The MOREPRO project is part of the same field of application. In particular, the goal is the prototyping of a control system for the monitoring of the cutting tool wear, this technology fits into a little explored field. Specifically, it refers to flexible production lines for monitoring the end-effector of a 5-axis milling machine. The MOREPRO project was born from the application of a successful control strategy obtained in an earlier Brain Technologies' project, called BATMAT. The innovative monitoring technique is the focal point of the project, to offer an advanced technology compared to the current market; indeed the MOREPRO project offers an alternative vision for the monitoring of machine tools. This project conveys different monitoring techniques to determine the optimal result in the processing field. Specifically, the innovations made by the project are mainly: • Distributed architecture on the production line to manage the different field variables, monitored by a supervisor. • The union of control technologies such as predictive strategies (e.g. Extended Kalman Filters), digital-twin, data driven techniques and machine learning applications. The first point aims to achieve a two-level architecture with a distribution of computing power on the line (edge computing technology), allowing these monitoring systems to be integrated with machine controls. In addition, the cloud-defined supervisor handles changes from different features to optimize and update on-the-board monitors. The second point focuses on the formalization of a predictive control system that integrates machine learning and data driven activities to ensure the accuracy of the carried-out checks and a self-updating ability. My thesis work carried out at Brain Technologies on the MOREPRO project, aims at the training of the figure of System Engineer. During the entire internship activity, I dealt with the formal definition of the project through acquired techniques, such as: definition of project requirements, graphic representation through schematization techniques, failure mode and effects analysis, design of experiments, verification and validation through tests. For a correct formal definition, it was necessary to understand the applications of the project from a technical point of view, collaborating with the other team members and my supervisor. An integral part of my role was related to the creation of an efficient communication between the working groups through graphical representations, with the use of software for a formal definition of the schemes; in addiction, the drafting of requirements with risk analysis, was useful for the software development process. After dealing with different phases of testing validation, it was possible to increase my technical qualities in MATLAB and Simulink, by iteratively performing Model in the Loop tests.

Relators: Stefano Carabelli, Giovanni Guida
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 135
Additional Information: Tesi secretata. Fulltext non presente
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/17836
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