Guo Dingli, Zhou Honggen, Sun Li, Li Guochao
School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212000, China.
Sensors (Basel). 2025 Jun 30;25(13):4068. doi: 10.3390/s25134068.
Remaining useful life (RUL) prediction of cutting tools plays an important role in modern manufacturing because it provides the criterion used in decisions to replace worn cutting tools just in time so that machining deficiency and unnecessary costs will be restrained. However, the performance of existing deep learning algorithms is limited due to the smaller quantity and low quality of labeled training datasets, because it is costly and time-consuming to build such datasets. A large amount of unlabeled data in practical machining processes is underutilized. To solve this issue, an unlabeled-data-enhanced tool RUL prediction method is proposed to make full use of the abundant accessible unlabeled data. This paper proposes a novel and effective method for utilizing unlabeled data. This paper defines a custom criterion and loss function to train on unlabeled data, thereby utilizing the valuable information contained in these unlabeled data for tool RUL prediction. The physical rule that tool wear increases with the increasing number of cuts is employed to learn knowledge crucial for tool RUL prediction from unlabeled data. Model parameters trained on unlabeled data contain this knowledge. This paper then transfers the parameters through transfer learning to another model based on labeled data for tool RUL prediction, thus completing unlabeled data enhancement. Since multiple sensors are frequently used to simultaneously collect cutting data, this paper uses a graph neural network (GNN) for multi-sensor data fusion, extracting more useful information from the data to improve unlabeled data enhancement. Through multiple sets of comparative experiments and validation, the proposed method effectively enhances the accuracy and generalization capability of the RUL prediction model for cutting tools by utilizing unlabeled data.
刀具剩余使用寿命（RUL）预测在现代制造业中起着重要作用，因为它为及时更换磨损刀具的决策提供了依据，从而抑制加工缺陷和不必要的成本。然而，由于标记训练数据集数量较少且质量较低，现有深度学习算法的性能受到限制，因为构建此类数据集成本高且耗时。实际加工过程中的大量未标记数据未得到充分利用。为了解决这个问题，提出了一种未标记数据增强的刀具RUL预测方法，以充分利用丰富的可获取未标记数据。本文提出了一种新颖有效的利用未标记数据的方法。本文定义了一个自定义准则和损失函数，用于对未标记数据进行训练，从而利用这些未标记数据中包含的有价值信息进行刀具RUL预测。利用刀具磨损随切削次数增加而增加的物理规律，从未标记数据中学习对刀具RUL预测至关重要的知识。在未标记数据上训练的模型参数包含这些知识。然后，本文通过迁移学习将这些参数转移到基于标记数据的另一个模型中进行刀具RUL预测，从而完成未标记数据增强。由于经常使用多个传感器同时采集切削数据，本文使用图神经网络（GNN）进行多传感器数据融合，从数据中提取更多有用信息，以改进未标记数据增强。通过多组对比实验和验证，所提方法通过利用未标记数据有效提高了刀具RUL预测模型的准确性和泛化能力。
