A high-precision and transparent step-wise diagnostic framework for hot-rolled strip crown

The strip crown plays a crucial role in determining the quality of products in strip hot rolling. Machine learning (ML) methods have shown promise in crown prediction by effectively capturing the nonlinearities and strong coupling present in hot rolling data, surpassing the capabilities of traditional methods. However, existing ML models ignore the imbalance of strip crown and tend to prioritize learning information from the qualified crown, limiting the precision of diagnosing the faulty crown. To overcome this limitation, a novel high-precision stepwise diagnostic framework is proposed. The framework starts with a crown detection module that promptly detects the faulty crown and enables timely blocking of the faulty strip. To enhance the diagnostic precision for the faulty crown, a novel hybrid data processing strategy that combines resampling method and cost-sensitive learning is introduced within the detection module, and the cost factor is optimized by Chaotic Harris Hawks Optimizer (CHHO). Subsequently, the framework incorporates a crown classification module to accurately recognize the specific fault-type present in the faulty strip. Furthermore, eXplainable Artificial Intelligence (XAI) technique is employed to ensure the transparent decision-making processes of both the detection module and the classification module. The comparative experiment results demonstrate that the proposed framework outperforms other state-of-the-art ML methods. It can achieve an excellent trade-off between precision and efficiency in diagnosing hot-rolled strip crown. Additionally, the feature contributions and decision interpretable analysis based on XAI provide further evidence of the transparency and effectiveness of the proposed framework.