Cutting tool wear is a critical factor that affects product quality in manufacturing processes and measuring the flank wear area is the most common method to assess the condition of the tool. Nowadays, the direct way based on image processing has been developed due to its high information content and does not rely on expensive subsidiary measurement equipment compared with the indirect way. However, the direct way, whether based on computer graphics or based on artificial intelligence, has a shortcoming. The traditional computer graphics methods have poor robustness, and the artificial intelligence way enabled with deep convolutional neural networks (DCNN) requires a large amount of data and it usually works well on its training data. This article proposes a new architecture based on active incremental fine-tuning, SegNet, and CRF. The new architecture integrates active incremental fine-tuning and conditional random field with an optimized SegNet. The new architecture has greatly improved the running speed and reduced the model size. Moreover, the architecture can be trained with small samples and obtain high precision. Finally, in our case, the architecture achieves an average accuracy rate of about 88% on a small dataset. The training process consumes about 4612 s, and the number of learning parameters is reduced to 788,006. The methodology in the article has been verified through experiments.

切削刀具的磨损是影响制造过程中产品质量的关键因素，而测量侧面磨损面积是评估刀具状况的最常见方法。如今，基于图像处理的直接方法由于其信息量高而被开发出来，与间接方法相比，它不再依赖昂贵的辅助测量设备。但是，直接方法，无论是基于计算机图形还是基于人工智能，都有一个缺点。传统的计算机图形方法的鲁棒性较差，而采用深度卷积神经网络（DCNN）的人工智能方法需要大量数据，并且通常可以很好地用于其训练数据。本文提出了一种基于主动增量式微调，SegNet和CRF的新体系结构。新架构将主动增量式微调和条件随机字段与优化的SegNet集成在一起。新架构极大地提高了运行速度并减小了模型尺寸。而且，可以用少量样本训练该体系结构并获得高精度。最后，在我们的案例中，该架构在一个小型数据集上实现了约88％的平均准确率。训练过程耗时约4612 s，学习参数的数量减少到788,006。本文中的方法已通过实验验证。最后，在我们的案例中，该架构在一个小型数据集上实现了约88％的平均准确率。训练过程耗时约4612 s，学习参数的数量减少到788,006。本文中的方法已通过实验验证。最后，在我们的案例中，该架构在一个小型数据集上实现了约88％的平均准确率。训练过程耗时约4612 s，学习参数的数量减少到788,006。本文中的方法已通过实验验证。