A Study on AI-Based Anomaly Detection and Classification Models for Phosphate Coating Thickness

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Key Points

The aim is to develop AI-based models for fast detection and classification of phosphate coating thickness to ensure adhesive performance in automotive applications.
Applied AI-based vision inspection technology considering batch characteristics of the coating process
Assessed the anomaly detection and classification models on large samples
Evaluated performance metrics of the models for real-time quality monitoring
Both anomaly detection and classification models achieved 100% detection and classification performance
Demonstrated practicality for real-time quality monitoring in phosphate coating processes
Outlined benefits of low initial investment and easy integration into existing production lines

Evaluates AI models for detecting phosphate coating thickness in steel products, suggesting efficient quality control solutions.

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Hwang et al. (2026) studied this question.

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