Fixture devices monitoring for machining condition optimisation aided by machine learning

Felipe Alves de Oliveira Perroni, Ugo Ibusuki, Eduardo de Senzi Zancul, Klaus Schützer, Cláudio Nogueira de Meneses and Thiago Cannabrava de Sousa

International Journal of Manufacturing Technology and Management, 2025, vol. 39, issue 3/4/5, 406-422

Abstract: This paper focuses on applying recent digitisation technologies for machining process improvement based on fixture device monitoring. Industry 4.0 technologies support smart monitoring of manufacturing processes, enabling semi-autonomous tool process parameters adjustment, reducing human-machine interactions, resulting in more accurate process improvements. The paper aims to present the results of a project development and validation of a machining conditioning monitoring system, combining measures conducted directly in the spindle unit and fixture devices. The machining condition monitoring system, aided by a machine learning algorithm, uses vibration data to determine the tool's maximum wear. The project, a collaborative effort by two universities, was designed for practical application and rigorously tested in a real-world operational environment at an automotive company.

Keywords: device monitoring; condition monitoring system; process optimisation; machine learning. (search for similar items in EconPapers)
Date: 2025
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