It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.

众所周知，切削条件会极大地影响加工精度和表面完整性。为了能够尽早更换切削刀具并采取积极措施，应提前估计刀具磨损状况并实时更新。在这项工作中，提出了一种基于深度学习方法的过程中工具状态预测方法。一个长期的短期记忆网络旨在根据历史数据预测多个侧面磨损值。利用在加工过程中获取的原始信号，建立了残差卷积神经网络以实现对过程中刀具状态的监视。它们的集成实现了对工具状态的预测。采用基于中值的校正和基于均值的校正来提高准确性。IEEE PHM 2010质询数据已用于说明和验证此方法。实验研究和定量比较表明，可以在加工过程中精确预测未来的侧面磨损值。所提出的方法有助于快速可靠地预测刀具状态，这将支持有关在数据驱动的智能制造中更换刀具的决策。