Honeycomb Core Milling Diagnosis using Machine Learning in the Industry 4.0 Framework

In an industrial environment, data driven smart diagnosis of the milling is a difficult task. In this work, the diagnosis using machine learning techniques has been developed and implemented for composite sandwich structures based on honeycomb core. The objective is to predict online the resulting surface flatness. The time domain and frequency domain features are calculated from the measured milling forces and different algorithms have been implemented and compared. The experimental results have shown that a good milling diagnosis can be obtained with a Linear Support Vector Machine (SVM) algorithm resulting to good accuracy and short training time.