有限样本下基于迁移学习的铣削稳定性预测方法

传统铣削稳定性分析因采用静态刀尖点频响函数和平均切削力系数而使其在真实工况下的预测精度降低。为此，引入迁移学习提出一种基于少量实验样本的铣削稳定性预测方法。首先，生成静态刀尖点频响函数和平均切削力系数在全转速范围内多个系列的随机值，并在各系列下进行铣削稳定性分析，通过计算少量极限切削深度实验值与对应的预测值之间的误差，确定最优系列并以其构造源域稳定域数据；然后，利用大量源域数据建立极限切削深度的预训练模型，通过少量实验样本全局微调此模型使其适应真实加工场景。以40组颤振实验样本展开实例验证，所提方法比采用少样本建模的预测精度提升32%,并对比不同数据规模下各类模型预测精度，共同验证所提方法的有效性。

Milling stability predictions under limited samples based on transfer learning

Traditional milling stability analysis has relatively low prediction accuracy under real working conditions for using the static tool tip frequency response functions (FRFs) and average cutting force coefficients. Therefore, a milling stability prediction method based on a small number of experimental samples is proposed by introducing transfer learning. First,the tool tip FRFs at idle state and the average cutting force coefficients are measured to generate multiple series of random values within the spindle speed range. An optimal series is determined by comparing the limited experimental stability limits and their related predicted values, and it is used to further construct sufficient source stability data close to the real data. On the basis, a multi-layer perceptron model for predicting the stability limits is formulated by the source data, and it is globally fine-tuned by the limited target experimental samples for adapting to the real machining scene. Forty groups of chatter experimental samples are used to develop a validation case study. The prediction accuracy of the proposed method is 32% higher than that of the model constructed only using the 40 samples. In addition, accuracies of different types of prediction models trained by different target data sizes are also compared to evaluate the effectiveness of the proposed method.