<p>The strip cold rolling process is a critical component of steel manufacturing, yet it is often plagued by challenges such as flatness defects, thickness variations, and vibrations, which can significantly impact productivity and product quality. Machine learning (ML) has emerged as a powerful tool to address these issues by analyzing vast amounts of process data to predict and mitigate potential defects in real-time. By leveraging historical and real-time data, ML algorithms can identify complex patterns and correlations between operational parameters—such as roll force, roll gap, and rolling speed—and key quality indicators like flatness and thickness uniformity. This enables the optimization of process parameters dynamically, ensuring consistent product quality while minimizing waste and downtime. Furthermore, ML-driven predictive models facilitate proactive adjustments to the rolling process, reducing the occurrence of defects and enhancing overall efficiency. The integration of ML not only improves the precision and reliability of the cold rolling process but also leads to substantial cost savings and increased productivity. By transforming traditional manufacturing practices, ML paves the way for smarter, more sustainable steel production, positioning the industry for future advancements in automation and digitalization.</p>