Article: An intelligent online detection approach based on big data for mechanical properties of hot-rolled strip Journal: International Journal of Modelling, Identification and Control (IJMIC) 2021 Vol.37 No.2 pp.106 - 112 Abstract: An LightGBM prediction model based on big data is presented in order to online detect the mechanical properties of hot-rolled strip in this paper, which can achieve the greater accuracy than both the existing prediction approaches and hardware detection method for the local strips. A dataset of mechanical properties of hot-rolled strip is constructed firstly by collecting a steel plant's hot-rolled process control parameters, which includes 17,000 samples, and every sample contains 17 input characteristics and three output mechanical property parameters. Based on the dataset, an LightGBM intelligent prediction model is established and trained to predict the three mechanical properties of the hot-rolled strip steels. 17,000 data of hot rolling mill are used to verify the effectiveness of the model. Results show that the prediction accuracy for tensile strength, compressive strength and elongation are 0.99971, 0.99835, and 0.99631, respectively. Especially, the prediction accuracy for elongation is higher than the existing methods. Inderscience Publishers - linking academia, business and industry through research

Title: An intelligent online detection approach based on big data for mechanical properties of hot-rolled strip

Authors: JinXiang Chen; Ziming Fan

Addresses: State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Iron & Steel Green and Intelligent Center, China Iron & Steel Research Institute Group, Beijing, 100081, China ' State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Automation Research and Design Institute of Metallurgical Industry, China Iron & Steel Research Institute Group, Beijing, 100081, China

Abstract: An LightGBM prediction model based on big data is presented in order to online detect the mechanical properties of hot-rolled strip in this paper, which can achieve the greater accuracy than both the existing prediction approaches and hardware detection method for the local strips. A dataset of mechanical properties of hot-rolled strip is constructed firstly by collecting a steel plant's hot-rolled process control parameters, which includes 17,000 samples, and every sample contains 17 input characteristics and three output mechanical property parameters. Based on the dataset, an LightGBM intelligent prediction model is established and trained to predict the three mechanical properties of the hot-rolled strip steels. 17,000 data of hot rolling mill are used to verify the effectiveness of the model. Results show that the prediction accuracy for tensile strength, compressive strength and elongation are 0.99971, 0.99835, and 0.99631, respectively. Especially, the prediction accuracy for elongation is higher than the existing methods.

Keywords: intelligent prediction; LightGBM; hot rolled strip; machine learning; big data analysis; steel mechanical properties.

DOI: 10.1504/IJMIC.2021.120210

International Journal of Modelling, Identification and Control, 2021 Vol.37 No.2, pp.106 - 112

Received: 04 Dec 2020
Accepted: 31 Dec 2020
Published online: 11 Jan 2022 *

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Title: An intelligent online detection approach based on big data for mechanical properties of hot-rolled strip

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