Efficient Cutting Power Modeling of Three-Axis Milling Based on Transfer Learning and Neural Network

The modeling of machining response like the cutting power has great significance for the simulation and optimization of the machining process before real physical cutting. Current cutting power models are usually constructed from some trial cutting experiments under specific cutting conditions, the model constructed under a cutting condition is difficult to apply in another one. To build another model, extensive re-trial cutting experiments should be conducted, which is time-consuming and costly. In this paper, an efficient cutting power modeling approach is proposed based on transfer learning. An instance & model hybrid transfer method for the domain adaption of data from two cutting conditions is proposed, from which the data of one cutting condition can reuse in the modeling process of the other cutting conditions. After the domain adaption process, a boosting technique is then applied that adaptively adjusts the weight of data from different cutting conditions. With the combination of the domain adaption and boosting technique, the cutting power model can be constructed efficiently. Experimental results from two case studies validate that, the cutting power models of three-axis milling as generated from the proposed approach have good prediction performance, which is much superior to the benchmarking algorithms in terms of improving the prediction accuracy and reducing the amount of data required for building the model.