用分层深度学习自动识别砂轮磨损状态，提升磨削精度。
Wear Classification of Abrasive Flap Wheels using a Hierarchical Deep Learning Approach
- 分三步识别：是否磨损、何种磨损类型、严重程度
- 准确率93.8%到99.3%，在真实图像上验证
- 适合自动化磨削系统中实时磨损监测
由于柔性特性，磨削用砂带轮常用于复杂自由曲面的精加工，但其磨损模式复杂，如凹凸形或砂带撕裂，影响加工效果。本文提出一种基于视觉的分层分类框架，实现砂带轮磨损状态的自动化监控。将问题分解为三个逻辑层级：（1）状态检测（新/旧），（2）磨损类型识别（矩形、凹形、凸形）与砂带撕裂检测，（3）严重程度评估（部分/完全变形）。构建了真实砂带轮图像数据集，采用EfficientNetV2的迁移学习方法。结果表明，分类准确率达93.8%（撕裂）至99.3%（凹形严重度）。通过梯度加权类激活映射（Grad-CAM）验证模型学习到物理相关特征，并分析误判案例。该分层方法为自动砂带轮磨削中的过程自适应控制和磨损补偿提供了基础。
原文摘要 · Abstract (English)
Abrasive flap wheels are common for finishing complex free-form surfaces due to their flexibility. However, this flexibility results in complex wear patterns such as concave/convex flap profiles or flap tears, which influence the grinding result. This paper proposes a novel, vision-based hierarchical classification framework to automate the wear condition monitoring of flap wheels. Unlike monolithic classification approaches, we decompose the problem into three logical levels: (1) state detection (new vs. worn), (2) wear type identification (rectangular, concave, convex) and flap tear detection, and (3) severity assessment (partial vs. complete deformation). A custom-built dataset of real flap wheel images was generated and a transfer learning approach with EfficientNetV2 architecture was used. The results demonstrate high robustness with classification accuracies ranging from 93.8% (flap tears) to 99.3% (concave severity). Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to validate that the models learn physically relevant features and examine false classifications. The proposed hierarchical method provides a basis for adaptive process control and wear consideration in automated flap wheel grinding.
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