At present, iron and steel enterprises have higher requirements for the stability of product quality, and there is an urgent need to establish a data-driven online monitoring model for strip product quality. In this paper, cubic splines are used and the standardized dimensionless data are fitted, and the standardized mean value of carbon content is 0.5932 and the STD value is 0.09775. The normalized average temperature of the soaking furnace is 0.2814 and the STD value is 0.1887. It can be seen that the correlation coefficient between the temperature and hardness of the soaking furnace is very large, and the correlation between carbon content and hardness is weak. Finally, the detection model that can realize the online detection of steel belt quality was successfully established, which made up for the shortcomings of the previous detection mode.

Wenjing Wang. Fitting optimization of steel product quality based on cubic splines. International Journal of Frontiers in Engineering Technology (2024), Vol. 6, Issue 6: 66-71. https://doi.org/10.25236/IJFET.2024.060610.

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