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. The dataset shared here is part of the research work published in Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.