In recent years, the growing popularity of artificial neural networks has urged more and more researchers to try introduce these methods to the machining field, with some of them actually producing good results. The acquisition of cutting data often means higher cost and time, limiting the application of neural network in the machining sector, to a certain extent. In this paper, for the task of cutting force prediction, a “transfer network” was established, based on data obtained by simulation, combined with the theory and method in the field of transfer learning. Compared to “ordinary network”, that is, traditional back-propagation neural network based on experimental samples alone, transfer network exhibits obvious performance advantages. On one hand, this means that, using the same experimental samples, the prediction error of transfer network will be controlled; while on the other hand, when the same prediction error is achieved, the number of experimental samples required by the transfer network will be less.

近年来，人工神经网络的日益普及促使越来越多的研究人员尝试将这些方法引入机械加工领域，其中一些方法实际上产生了良好的效果。切削数据的获取通常意味着更高的成本和时间，从而在一定程度上限制了神经网络在加工领域的应用。本文针对切削力的预测任务，结合仿真学习中的理论和方法，基于仿真得到的数据，建立了一个“传输网络”。与“普通网络”（即仅基于实验样本的传统​​反向传播神经网络）相比，传输网络具有明显的性能优势。一方面，这意味着使用相同的实验样本，控制传输网络的预测误差；另一方面，当达到相同的预测误差时，传输网络所需的实验样本数量将减少。