Academic Journal of Computing & Information Science, 2025, 8(6); doi: 10.25236/AJCIS.2025.080610.
Jiao Tang1, Yixuan Zhang1, Yanzhuo Wu1, Qingkun Yu2
1School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China, 114051
2College of Science, University of Science and Technology Liaoning, Anshan, China, 114051
In the steel industry, the mechanical properties of cold-rolled strips are crucial for product quality and cost. This study aims to optimize the continuous annealing process parameters. Data preprocessing involved identifying and removing outliers using box-and-line plots and addressing missing values with a decision tree-filling method. Descriptive analysis was performed to determine the statistical characteristics of the process parameters, and normality was assessed using P-P plots, Q-Q plots, and histograms, revealing that the data did not follow a normal distribution. The Spearman correlation coefficient was used to examine parameter correlations, identifying those that significantly affect strip hardness based on their significance levels. A Decision Tree machine learning model was then employed to analyze feature importance, identifying six key parameters influencing strip hardness. The top three factors were the temperature of the fast-cooling furnace (29.1%), carbon content (25.1%), and the temperature of the quenching furnace. Notably, the importance of fast-cooling furnace temperature and carbon content was equal, while strip speed was the least influential factor, accounting for only 3.2%. This research provides essential insights and practical references for optimizing the production process and quality control of cold rolled strip steel.
Process Parameter Analysis, Statistical Characterization, Strip Hardness, Decision Tree Modeling
Jiao Tang, Yixuan Zhang, Yanzhuo Wu, Qingkun Yu. Study on the Influence of Process Parameters in Cold-Rolled Strip Using Statistical and Machine Learning Methods. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 6: 84-89. https://doi.org/10.25236/AJCIS.2025.080610.
[1] Fu Wenlong, Zhao Yibo, Fu Yuchen, et al. Prediction of dissolved gas volume fraction in transformer oil based on improved box and line diagram and ISMAHHO-KELM [J/OL]. High Voltage Electrical Apparatus, 2024, 1-13 [2024-11-30].
[2] Zong Xuping, Yao Yulan. Quickly check the statistical distribution of data using Q-Q and P-P plots [J]. Statistics and Decision Making, 2010, (20): 151-152.
[3] Ejegwa, Paul Augustine, Kausar, Nasreen, Aydin, Nezir, et al.A new Fermatean fuzzy Spearman-like correlation coefficient and its application in evaluating insecurity problem via multi-criteria decision-making approach [J]. Heliyon, 2024, 10(22).
[4] Zidong Xu, Zhenyong Zhang. Power system anomaly detection method based on feature importance of unbalanced high-dimensional data [J]. Control Engineering, 2024, 31(11): 2029-2035.
[5] Yi Jingjing, Li Xingting, Pu Chunrong, et al. Risk warning model for postoperative adverse pregnancy outcomes in patients with cervical insufficiency based on decision tree algorithm [J/OL]. Chongqing Medicine, 2024, 1-8 [2024-11-30].