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.).

Industry 4.0 Readiness Calculation—Transitional Strategy Definition by Decision Support Systems

“…Water footprints, pollution level, and global warming impact measuring are other challenges Industry 4.0 tends to deal with in this sector, while Canizares and Valero [ 19 ] found that the improvements of using IoT technologies in metal machining are very high resulting in the higher efficiency and cost reduction. Maier et al [ 20 ] and Knittel et al [ 21 ] have noticed similar benefits from digitization of the tools.…”

Recent Advances on Machine Learning Applications in Machining Processes

“…Glatt et al [80] predicted the martensite content after cryogenic turning, considering the passive, cutting and feed forces and temperature, applying the PCC approach for feature selection and obtaining a RMSE of~0.8. Linear kernel was also applied for flatness classification prediction in honeycomb cores using the time-domain and frequency-domain (based on FFT) features of the force signal and the PCA for feature reduction [81,82]. Finally, Nain et al [83] implemented a Gaussian Process with Polynomial Kernel to predict the SR peculiarities of WEDM.…”

Machine Learning based Real Time Predictive Maintenance at the Edge for Manufacturing Systems: A Practical Example

“…Also, several leading scientific journals are specifically dedicated to this research field involving many researchers and engineers from different disciplines: general computing, computer networks and IoT (Internet of Things), advanced statistics, signal processing, Artificial Intelligence, data science and data mining, intensive computing, embedded systems, etc.. Some diagnostic approaches are specifically designed or adapted for application fields and sectors such as manufacturing and production systems [3], industry 4.0 [4], material/tools interaction in milling [5] [6] [7], aeronautic sector [8], energy [9] [ 10], health [11], mobility/transport [12], waste management/wastewater treatment plants [13], rotating machines [14], … Predictive diagnostics and maintenance approaches are traditionally based on different methods:…”