Mechanics & Industry
Volume 20, Number 8, 2019
Selected scientific topics in recent applied engineering – 20 Years of the ‘French Association of Mechanics – AFM’
|Number of page(s)||9|
|Published online||21 July 2020|
Milling diagnosis using artificial intelligence approaches
LEM3 UMR CNRS 7329, Institute Mines-Telecom GIP-InSIC, University of Lorraine, 27 rue d'Hellieule, 88100 Saint Dié des Vosges, France
2 University of Strasbourg, Faculty of Physics and Engineering, 3 rue de l'Université, 67000 Strasbourg, France
* e-mail: firstname.lastname@example.org
Accepted: 15 June 2020
The Industry 4.0 framework needs new intelligent approaches. Thus, the manufacturing industries more and more pay close attention to artificial intelligence (AI). For example, smart monitoring and diagnosis, real time evaluation and optimization of the whole production and raw materials management can be improved by using machine learning and big data tools. An accurate milling process implies a high quality of the obtained material surface (roughness, flatness). With the involvement of AI-based algorithms, milling process is expected to be more accurate during complex operations. In this work, a milling diagnosis using AI approaches has been developed for composite sandwich structures based on honeycomb core. The use of such material has grown considerably in recent years, especially in the aeronautic, aerospace, sporting and automotive industries. But the precise milling of such material presents many difficulties. The objective of this work is to develop a data-driven industrial surface quality diagnosis for the milling of honeycomb material, by using supervised machine learning methods. In this approach cutting forces are online measured in order to predict the resulting surface flatness. The developed diagnosis tool can also be applied to the milling of other materials (metal, polymer, etc.).
Key words: Milling diagnosis / machine learning / support vector machine (SVM) / artificial intelligence / honeycomb core
© D. Knittel et al., published by EDP Sciences 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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