Data analytics and uncertainty quantification for energy prediction in manufacturing

Many industries are applying various methods for optimizing energy use across the manufacturing life cycle. These methods are either physics-based or data-driven. Manufacturing systems generate a vast amount of data from operations and in simulations. Advances in data collection systems and data analytics (DA) tools have enabled the development of predictive analytics for energy prediction. Many of these prediction methods do not account for the uncertainty quantification-UQ (both in data and model). This work addresses the issue of uncertainty in predictive analytics. This work focuses on metal cutting processes and presents a Neural Networks (NNs) model to predict the required energy consumption during the manufacturing of a part on a milling machine. The model accounts for the uncertainty associated with both the manufacturing processes parameters, and assumptions in building the prediction model. To achieve this, prediction intervals are estimated instead of point predictions. In order to increase the ability to generalize over new datasets, an ensemble model of neural networks (NNs) is used, and the k-nearest-neighbors (k-nn) approach is applied to identify similar patterns between training and test datasets to increase the accuracy of the results by using local information from the closest patterns of the training sets. Case study results demonstrate consistency and high prediction precision as compared to the individual NNs of the ensembles. Moreover, it is shown that with advanced data collection and processing techniques, one can construct a prediction model to predict the energy consumption of a machine tool for machining a part with multiple operations and process parameters.