Wear Classification of Abrasive Flap Wheels using a Hierarchical Deep Learning Approach

TL;DR

This paper introduces a hierarchical deep learning framework for classifying wear conditions of abrasive flap wheels, enabling automated monitoring and improved process control in grinding operations.

Contribution

It presents a novel multi-level classification approach using transfer learning and a custom dataset, enhancing accuracy and interpretability over traditional monolithic methods.

Findings

Achieved up to 99.3% accuracy in wear classification

Validated model focus on physically relevant features with Grad-CAM

Demonstrated robustness across different wear conditions

Abstract

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.