Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images

Area of Science:

• Manufacturing Engineering
• Artificial Intelligence
• Materials Science

Background:

• Cutting tool wear monitoring is crucial in manufacturing to prevent workpiece waste and machine damage.
• Existing methods may lack efficiency and precision in real-time tool wear assessment.
• Intelligent systems are needed for accurate and timely tool wear state monitoring.

Purpose of the Study:

• To develop an efficient and precise intelligent system for workpiece surface-based tool wear monitoring.
• To ensure timely tool changes and avoid issues related to excessive tool wear or breakage.
• To introduce the efficient channel attention destruction and construction learning (ECADCL) method for this purpose.

Main Methods:

• Employed an end-to-end improved fine-grained image classification method (ECADCL).
• Utilized a feature extraction module with adversarial learning to extract semantic features from images and their corrupted versions, avoiding noise.
• Integrated a local cross-channel interaction attention mechanism without dimensionality reduction for robust feature characterization.
• Developed a decision module for predicting tool wear labels based on learned features.

Main Results:

• A milling dataset of machined surface images was created for tool wear monitoring.
• Experimental results demonstrated the effectiveness of the ECADCL method in assessing tool wear states.
• The proposed system accurately monitored tool wear conditions based on workpiece surface images.

Conclusions:

• The developed ECADCL system provides an effective solution for cutting tool wear state assessment.
• The intelligent system enhances manufacturing process efficiency by enabling timely tool management.
• This approach contributes to reducing material waste and preventing potential machine damage in manufacturing operations.