Integrating large language models with explainable fuzzy inference systems for trusty steel defect detection

In industrial applications, the complexity of machine learning models often makes their decision-making processes difficult to interpret and lack transparency, particularly in the steel manufacturing sector. Understanding these processes is crucial for ensuring quality control, regulatory compliance, and gaining the trust of stakeholders. To address this issue, this paper proposes LE-FIS, a large language models (LLMs)-based Explainable Fuzzy Inference System to interpret black-box models for steel defect detection. The method introduces a locally trained, globally predicted deep detection approach (LTGP), which segments the image into small parts for local training and then tests on the entire image for steel defect detection. Then, LE-FIS is designed to explain the LTGP by automatically generating rules and membership functions, with a genetic algorithm (GA) used to optimize parameters. Furthermore, state-of-the-art LLMs are employed to interpret the results of LE-FIS, and evaluation metrics are established for comparison and analysis. Experimental results demonstrate that LTGP performs well in defect detection tasks, and LE-FIS supported by LLMs provides a trustworthy and interpretable model for steel defect detection, which enhances transparency and reliability in industrial environments.