MS-DenseNet-GRU tool wear prediction method based on attention mechanism

Yaonan Cheng, Jing Xue, M. Lu, Shilong Zhou, Xiaoyu Gai, R. Guan
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Abstract

Tool wear was an inevitable physical phenomenon in the cutting procedure. Serious tool wear has a direct effect on the level of processing quality and the effectiveness of production, and it even leads to abnormal cutting processes and a series of safety problems. Effective tool wear prediction can provide a basis for the rational use and replacement of tools to improve tool efficiency and ensure the stable operation of the machining process. Therefore, a tool wear prediction method combining multiple deep learning modules was proposed. To begin, the vibration signal was broken up using the complete ensemble empirical mode decomposition with adaptive noise algorithm. Then, the intrinsic mode functions with a strong correlation with the original signal were screened out according to the Pearson correlation coefficient for signal reconstruction. Additionally, the DenseNet module, the gate recurrent unit (GRU) module and the efficient channel attention module were deeply integrated to build a multi-scale DenseNet-GRU tool wear prediction model with attention mechanisms by learning the relationship of mapping between signal features and tool wear. Finally, the model was trained and tested using milling experimental data. The experiments’ outcomes demonstrated that the suggested method can accurately and reliably estimate the tool wear value. Compared with the DenseNet model, convolutional neural network–long short-term memory model, and DenseNet-GRU model, it further shows that it had superior performance in prediction accuracy and generalization ability. The research results can provide certain technical support for the prediction of tool wear intelligently, which is vital to raising the quality of processing, reducing production costs, and promoting the manufacturing industry’s intelligent development.
基于注意力机制的 MS-DenseNet-GRU 工具磨损预测方法
刀具磨损是切削过程中不可避免的物理现象。严重的刀具磨损会直接影响加工质量水平和生产效益,甚至导致切削过程异常和一系列安全问题。有效的刀具磨损预测可以为合理使用和更换刀具提供依据,从而提高刀具的使用效率,保证加工过程的稳定运行。因此,本文提出了一种结合多个深度学习模块的刀具磨损预测方法。首先,使用带有自适应噪声算法的完全集合经验模态分解法对振动信号进行分解。然后,根据皮尔逊相关系数筛选出与原始信号具有较强相关性的本征模态函数,进行信号重构。此外,DenseNet 模块、门递归单元(GRU)模块和高效通道注意模块被深度集成,通过学习信号特征与刀具磨损之间的映射关系,建立了具有注意机制的多尺度 DenseNet-GRU 刀具磨损预测模型。最后,利用铣削实验数据对模型进行了训练和测试。实验结果表明,所建议的方法可以准确可靠地估计刀具磨损值。与 DenseNet 模型、卷积神经网络-长短期记忆模型和 DenseNet-GRU 模型相比,该方法在预测精度和泛化能力方面都有更优越的表现。该研究成果可为刀具磨损的智能预测提供一定的技术支持,对提高加工质量、降低生产成本、促进制造业智能化发展至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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