Machine Learning solution for machining quality prediction using acoustic emissions, accelerometers and current data

This outcome contains the dataset used to train tool wear & quality prediction algorithms in a milling context. The data model is composed of three data sources (acoustics emission, accelerometers & currents). The acoustics emissions and accelerometers are recorded on an external machine which needs a post-synchronization. The currents are recorded by the system that directly monitors the machine. Acoustics emission (file: toolwear_2020_ae.zip): In a previous data acquisition (not published), the acoustic emission was recorded using three sensors (on the raw part, on the axis closest to the part & outside the machine), but in this dataset, it is only composed of one sensor positioned inside the machine. The sampling rate is 200kHz. Accelerometers (file: toolwear_2020_acc.zip): The accelerometer dataset is composed of nine different sensors with an acquisition frequency of 20kHz. One signal is dedicated to the synchronization between other data sources (acoustics emission & currents). Five signals are installed on the spindle axis, two (XY directions) on the top of the spindle and three (XYZ directions) on the bottom of the axis. The last three (XYZ directions) are located on the axis nearest to the part. Currents (file: toolwear_2020_axes.zip): The machine is composed of five axes and one spindle. For each motor, the current is acquired and stored into the monitoring system of the CNC. The frequency data acquisition is 1kHz.