基于域对抗门控网络的变工况刀具磨损精确预测方法

刀具磨损的精确预测对保证零件加工质量、提高生产效率和降低制造成本具有重要作用。在实际加工过程中，切削参数、刀具几何参数、刀具材料等工况复杂多变，工况信息和刀具磨损量对监测信号的耦合作用为刀具磨损的精确预测带来了很大挑战。针对以上问题，提出了一种基于域对抗门控网络（DAGNN）的变工况刀具磨损精确预测方法。引入工况分类网络并利用无磨损量标签样本，通过域对抗和门控过滤机制自适应地从不同工况的原始监测信号中提取表征刀具磨损且对工况变化不敏感的关键信号特征。对信号特征提取网络和刀具磨损预测网络进行迭代优化，从而实现变工况刀具磨损的精确预测。实验结果表明：相比已有的方法，本文方法能够利用少量带磨损量标签的目标工况样本实现刀具材料和刀具直径变化情况下的刀具磨损量精确预测，预测精度大幅提高。

Method for accurate prediction of tool wear under varying cutting conditions based on domain adversarial gating neural network

The accurate prediction of tool wear plays an important role in ensuring the quality of parts, improving production efficiency and reducing manufacturing costs. In the actual machining process, cutting parameters, tool geometric parameters, tool materials and other cutting conditions are complex and variable. The coupling effect of cutting condition and tool wear on monitoring signals brings great challenges to accurate prediction of tool wear. To address the above issue, this paper proposes a method for accurate prediction of tool wear under variable cutting conditions based on Domain Adversarial Gating Neural Network (DAGNN). A cutting condition classification network is trained using unlabeled samples, and key signal features, which can represent tool wear and are not sensitive to cutting conditions, are adaptively extracted from the original monitoring signals of different cutting conditions through domain adversarial and gated filtering mechanisms. The signal feature extraction network and tool wear prediction network are iteratively optimized to achieve accurate prediction of tool wear under variable cutting conditions. Experimental results show that compared with existing methods, the method proposed can achieve higher prediction accuracy even when only a few wear labeled samples of target cutting condition are available.