Authors: Fengyi Lu, Guanghui Zhou, Yang Liu, Chao Zhang.
Publication date: 2021

Cutting energy consumption prediction gives decision support for the energy-saving operation to realize green manufacturing. However, there are challenges when predicting aviation parts due to the machining features causing tool wear and expensive data labeling. Consequently, this paper builds a prediction model and emphasizes training with limited experimental data by proposing an ensemble transfer learning approach. The approach incorporates transfer learning, i.e., TrAdaBoost-R2 (TR) algorithm, and calibration, i.e., Bayesian and Markov chain Monte Carlo calibration (MCMC).