Online Prediction Model for Cold-Rolled Strip Steel Hardness based on Simulated Annealing and Neural Network

Abstract

In recent years, the booming automotive, home appliance, and construction industries have driven a surge in market demand for cold-rolled steel strips. In response to the “carbon neutrality” initiative, the steel industry has accelerated its green transformation. The high added-value characteristics of cold-rolled strip steel highlight the importance of quality control. However, its production process is complex, with intertwined parameters. This paper aims to establish a model that can accurately reflect the production mechanism. For Problem 1, this paper innovatively combines principal component analysis (PCA) with the Pearson correlation coefficient to precisely screen out the key parameters that have a decisive impact on hardness. For Problem 2, a composite model based on random forest and BP neural network is constructed. This model can capture the subtle changes in production data in real time and accurately predict product quality, providing strong support for production decision-making. For Problem 3, the advanced optimization tool of simulated annealing algorithm is introduced. Through the intelligent optimization of complex process parameters, improvements in production efficiency and product quality are achieved. For Problem 1, our objective focused on identifying the key parameters significantly affecting the hardness of cold-rolled steel strips. In the preliminary data processing stage, we addressed missing values and outliers through linear interpolation and moving median filtering, followed by data standardization using the Z-score method. This rigorous preprocessing established a reliable data foundation for subsequent modeling. We then employed two complementary statistical approaches for feature selection and model development: PCA and Pearson correlation analysis. PCA identified critical variables based on their contribution rates to variance, while Pearson correlation coefficients quantitatively assessed the strength of linear relationships. These mutually validating methodologies collectively established robust correlations between hardness and twelve key process parameters. The final identified significant parameters include: thickness, width, carbon content, silicon content, strip steel speed, heating furnace temperature, soaking furnace temperature, slow cooling furnace temperature, over-aging furnace temperature, rapid cooling furnace temperature, quenching temperature, and temper mill tension. For Problem 2, which requires establishing a data-driven online quality inspection model for steel strips and analyzing its performance, we first introduced the random forest algorithm to significantly enhance model effectiveness and computational efficiency. Through this approach, we extracted the most representative feature subset, revealing that carbon content and rapid cooling furnace temperature are the most critical factors in predicting steel strip product quality, followed by physical dimensions, with silicon content and other heat treatment parameters also exhibiting certain influence. Building upon this optimized feature subset, we employed a backpropagation neural network to construct the online prediction model. Through hyperparameter tuning, we optimized the neural network architecture configuration, determining that 12 hidden layers are optimal for the acceleration/deceleration phases while 9 hidden layers achieve peak performance during stable operation phases. Finally, we conducted efficiency optimization and performance evaluation of the model, with results demonstrating excellent prediction performance at 97.6733% accuracy, prediction errors within 5%, and overall satisfactory prediction outcomes. For Problem 3, which requires establishing a comprehensive and efficient solution for optimizing process parameters of steel strips, simulated annealing algorithm was adopted in this study to globally search for optimal solutions, given the difficulties in developing mechanistic models due to the complex interdependencies among control parameters. The methodology encompasses parameter initialization, key parameter identification, objective function formulation, constraint handling, result validation, and model integration. Building upon the results of Problem 1, we first identified critical parameters and construct the objective function, followed by setting practical production constraints. The simulated annealing algorithm performs global optimization while effectively addressing constraint limitations through a penalty function mechanism. The optimal solution yields: strip speed of 201 m/s, heating furnace temperature at 710°C, soaking furnace temperature at 645°C, slow cooling furnace temperature at 608°C, over-aging furnace temperature at 355°C, rapid cooling furnace temperature at 67°C, quenching temperature at 45°C, and temper mill tension at 2420 kN, achieving optimal mechanical properties under these conditions.