School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China.
Science and Technology Information Department, Shaanxi Diesel Heavy Industry Co. Ltd, Xi'an, 713105, China.
Sci Rep. 2022 Aug 26;12(1):14574. doi: 10.1038/s41598-022-18235-3.
Evaluation of tool wear is vital in manufacturing system, since early detections on worn-out condition can ensure workpiece quality, improve machining efficiency. With the development of intelligent manufacturing, tool wear prediction technology plays an increasingly important role. However, traditional tool wear prediction methods rely on experience and knowledge of experts and are labor-extensive. Deep learning provides an effective way to extract features of raw data and establish the mapping relationship between features and targets automatically. In this paper, a new local-feature and global-dependency based tool wear prediction method is proposed. It is a hybrid approach combining manual features with automatic features. Firstly, an enhanced CNN network is designed and applied on the transformed wavelet scalogram to learn the local single-scale specific features and multi-scale correlation features automatically. Secondly, sequence of local feature vectors combining manual features with automatic features are fed into multi-layer LSTM step by step for the global dependency. A fully connected layer is then trained to predict tool wear. Finally, two statistics are proposed to illustrate the overall prediction performance and generalization ability of the model. An experiment illustrates the effectiveness of our proposed method under multiple working conditions.
在制造系统中，工具磨损的评估至关重要，因为及早发现磨损状态可以确保工件质量，提高加工效率。随着智能制造的发展，工具磨损预测技术的作用越来越重要。然而，传统的工具磨损预测方法依赖于专家的经验和知识，并且非常耗费人力。深度学习为提取原始数据的特征并自动建立特征与目标之间的映射关系提供了一种有效的方法。本文提出了一种基于局部特征和全局相关性的新型工具磨损预测方法，是一种结合手动特征和自动特征的混合方法。首先，设计了一个增强的 CNN 网络，并将其应用于变换后的小波谱图，以自动学习局部单尺度特定特征和多尺度相关特征。其次，将结合手动特征和自动特征的局部特征向量序列逐步输入到多层 LSTM 中，以获取全局相关性。然后，训练一个全连接层来预测工具磨损。最后，提出了两个统计指标来说明模型的整体预测性能和泛化能力。实验在多种工况下验证了所提方法的有效性。
