Milling diagnosis using artificial intelligence approaches

Abstract

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.).