A novel data concept for cutting processes through comprehensive experimental setup enabling grey-box models

This paper presents a novel experimental setup for recording the cutting process of external longitudinal turning in situ, capturing data such as cutting force, vibration, acoustic emission (AE), rake face temperature, surface quality, and tool wear. The primary objective of this setup is to generate reliable, repeatable data for developing a comprehensive grey-box model. To achieve this, the measurements were partly automated. Additionally, a complete post-processing pipeline has been introduced to combine all relevant data. To validate the setup, external longitudinal turning experiments were conducted and two of these were analysed for sensitivity to tool wear. This was achieved by analysing the mean and variance of the resultant force FZ, resultant vibrations aZ and AE signal, in order to demonstrate the impact of tool wear on the measurement. The experiments were also used to train an autoencoder to analyse the process data.