Accurate prediction of the energy consumption is critical for energy-efficient production systems. However, the majority of the existing prediction models aim at providing only point predictions and can be affected by the uncertainties in the model parameters and input data. In this paper, we propose a prediction model generating prediction intervals (PIs) for estimating energy consumption of a milling machine. PIs are used to provide information on the confidence in the predictions accounting for both the uncertainty in the model parameters and the noise in the input variables. We use an ensemble model of neural networks (NNs) to estimate PIs. We apply a k-nearest neighbors (k-nn) approach to identify similar patterns between training and testing sets to increase the accuracy of the results by using local information from the closest patterns of the training sets. Finally, we present a case study that uses a dataset obtained by machining 18 parts through face-milling, contouring, slotting and pocketing, spiraling, and drilling operations. Of these six operations, the case study focuses on face milling to demonstrate the effectiveness of the proposed energy prediction model.
August 2-5, 2015
ASME 20th Design for Manufacturing and the Life Cycle Conference (DFMLC)
Energy prediction, Ensemble, Manufacturing, Milling, Neural networks, Prediction intervals.