Yang Jing, Duan Jian, Li Tianxiang, Hu Cheng, Liang Jianqiang, Shi Tielin
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China.
Sensors (Basel). 2022 Nov 2;22(21):8416. doi: 10.3390/s22218416.
Cutting tool wear state assessment during the manufacturing process is extremely significant. The primary purpose of this study is to monitor tool wear to ensure timely tool change and avoid excessive tool wear or sudden tool breakage, which causes workpiece waste and could even damage the machine. Therefore, an intelligent system, that is efficient and precise, needs to be designed for addressing these problems. In our study, an end-to-end improved fine-grained image classification method is employed for workpiece surface-based tool wear monitoring, which is named efficient channel attention destruction and construction learning (ECADCL). The proposed method uses a feature extraction module to extract features from the input image and its corrupted images, and adversarial learning is used to avoid learning noise from corrupted images while extracting semantic features by reconstructing the corrupted images. Finally, a decision module predicts the label based on the learned features. Moreover, the feature extraction module combines a local cross-channel interaction attention mechanism without dimensionality reduction to characterize representative information. A milling dataset is conducted based on the machined surface images for monitoring tool wear conditions. The experimental results indicated that the proposed system can effectively assess the wear state of the tool.
制造过程中的刀具磨损状态评估极其重要。本研究的主要目的是监测刀具磨损，以确保及时更换刀具，避免刀具过度磨损或突然断裂，因为这会导致工件报废，甚至可能损坏机床。因此，需要设计一个高效且精确的智能系统来解决这些问题。在我们的研究中，一种端到端改进的细粒度图像分类方法被用于基于工件表面的刀具磨损监测，该方法被称为高效通道注意力破坏与构建学习（ECADCL）。所提出的方法使用一个特征提取模块从输入图像及其损坏图像中提取特征，并通过重建损坏图像来避免在提取语义特征时从损坏图像中学习噪声，同时使用对抗学习。最后，一个决策模块基于学习到的特征预测标签。此外，特征提取模块结合了一个无降维的局部跨通道交互注意力机制来表征代表性信息。基于加工表面图像进行了一个铣削数据集实验，以监测刀具磨损情况。实验结果表明，所提出的系统能够有效地评估刀具的磨损状态。
