The multi sensor-based machining signal fusion to compare the relative efficacy of machine learning based tool wear models

This dataset contains a force dynamometer, accelerometer sensor, acoustic emission sensor, and tool wear values for different milling conditions. For each condition, 12 experiments were conducted. Tool 1 (T1) to Tool 4 (T4) were used to develop the machine learning models and is validated with Tool 5 (T5) to Tool 8 (T8) respectively. This dataset contains raw data taken from each sensor output for each experimental cut. From this dataset, the relative efficacy of machine learning-based tool wear models was developed. Also, two sensor combination was used to compare the sensor effectiveness in tool wear prediction.