Multi-sensor based power prediction models for different geometrical profiles using Box Behnken Design on Inconel 718

This dataset includes measurements from various sensors - force dynamometer, accelerometer, acoustic emission sensor - and power readings for different geometric shapes machined on Inconel 718. The Box-Behnken design methodology was used to conduct the experiment.

The data was then employed for two purposes: developing and validating a model, and testing that model with unseen machining conditions using a separate tool.

The dataset contains raw sensor outputs for each experiment, allowing researchers to compare the effectiveness of machine learning and deep learning models for predicting power consumption. Additionally, researchers were able to assess the power consumption per unit of material removed for each geometric profile.