Research on Strip Steel Process Optimization Based on Data-Driven Approach and Genetic Algorithm

Abstract

In the context of modern manufacturing, ensuring consistent product quality while optimizing production processes is a significant challenge. This paper presents a comprehensive approach to improving the quality inspection and process optimization of steel strip production using machine learning models and genetic algorithms. Historical production data were pre-processed through cleaning, feature selection, and z-score normalization to ensure accuracy in the model's performance. Two classification models, Logistic Regression and K-Nearest Neighbors (KNN), were evaluated for their ability to predict product quality. The Logistic Regression model outperformed KNN with an accuracy of 0.36, making it the preferred choice for the online inspection system. To optimize the steel strip production process and achieve the target hardness value, a Genetic Algorithm (GA) was applied to determine the best combination of process parameters. The GA simulation considered key factors such as strip thickness, width, carbon and silicon content, and furnace temperatures. The results demonstrate that the integration of a predictive model and optimization algorithm provides a data-driven solution for enhancing product quality and operational stability in steel strip manufacturing.