Xue Wen, Zhao Chenyang, Fu Wenpeng, Du Jianjun, Yao Yingxue
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
Sensors (Basel). 2022 Mar 22;22(7):2426. doi: 10.3390/s22072426.
Nowadays, tool condition monitoring (TCM), which can prevent the waste of resources and improve efficiency in the process of machining parts, has developed many mature methods. However, TCM during the production of cutting tools is less studied and has different properties. The scale of the defects in the tool production process is tiny, generally between 10 μm and 100 μm for diamond tools. There are also very few samples with defects produced by the diamond tool grinding process, with only about 600 pictures. Among the many TCM methods, the direct inspection method using machine vision has the advantage of obtaining diamond tool information on-machine at a low cost and with high efficiency, and the method is accurate enough to meet the requirements of this task. Considering the specific, above problems, to analyze the images acquired by the vision system, a neural network model that is suitable for defect detection in diamond tool grinding is proposed, which is named DToolnet. DToolnet is developed by extracting and learning from the small-sample diamond tool features to intuitively and quickly detect defects in their production. The improvement of the feature extraction network, the optimization of the target recognition network, and the adjustment of the parameters during the network training process are performed in DToolnet. The imaging system and related mechanical structures for TCM are also constructed. A series of validation experiments is carried out and the experiment results show that DToolnet can achieve an 89.3 average precision (AP) for the detection of diamond tool defects, which significantly outperforms other classical network models. Lastly, the DToolnet parameters are optimized, improving the accuracy by 4.7%. This research work offers a very feasible and valuable way to achieve TCM in the manufacturing process.
如今，刀具状态监测（TCM）已发展出许多成熟的方法，它可以防止资源浪费并提高零件加工过程的效率。然而，切削刀具生产过程中的刀具状态监测研究较少且具有不同特性。刀具生产过程中缺陷的尺寸非常小，对于金刚石刀具来说，一般在10μm至100μm之间。金刚石刀具磨削过程产生的有缺陷样本也非常少，只有大约600张图片。在众多刀具状态监测方法中，使用机器视觉的直接检测方法具有低成本、高效率地在机床上获取金刚石刀具信息的优势，并且该方法足够精确，能够满足这项任务的要求。考虑到上述具体问题，为了分析视觉系统采集的图像，提出了一种适用于金刚石刀具磨削缺陷检测的神经网络模型，命名为DToolnet。DToolnet通过从小样本金刚石刀具特征中提取和学习来开发，以便直观快速地检测其生产过程中的缺陷。在DToolnet中，进行了特征提取网络的改进、目标识别网络的优化以及网络训练过程中的参数调整。还构建了用于刀具状态监测的成像系统和相关机械结构。进行了一系列验证实验，实验结果表明，DToolnet在金刚石刀具缺陷检测方面的平均精度（AP）可达89.3%，显著优于其他经典网络模型。最后，对DToolnet参数进行了优化，精度提高了4.7%。这项研究工作为在制造过程中实现刀具状态监测提供了一种非常可行且有价值的方法。
