Jun Chen
6 Papers
13 Citations
Jun Chen is an academic researcher. The author has contributed to research in topics: Continuous casting & Multicollinearity. The author has an hindex of 3, co-authored 6 publications.
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Papers
Optimization of Thermal Soft Reduction on Continuous-Casting Billet
TL;DR: In this paper, a heat transfer model with comprehensive thermo-physical parameters was established to simulate the thermal behavior of continuous-casting billet, and thermal soft reduction (TSR) was optimized to ensure its effect and control the cracks of 82A tire cord steel billet.
Comparison and integration of final electromagnetic stirring and thermal soft reduction on continuous casting billet
TL;DR: In this article, a heat transfer model was established to calculate the thermal behavior of 82A tire cord steel billet, and the integration of F-EMS and TSR allowed the advantages of each technique to be utilized, thereby better improving the inner quality.
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Behaviour of oxide inclusions and sulphur in ‘two-stage basicity control’ refining method of Si-killed spring steel
TL;DR: In this paper, a two-stage basicity control method was carried out that high basicity (R < 1.5) top slag was used in the LF refining and then low basicity(R < 0.8 − 1.0, Al2O3 < 0, 6%) top slags was used.
11
Patent
Spring steel austenite grain display method
Liu Qing,Wang Chen,Wang Xingyu,Han Yanshen,Zhang Jiangshan,Leilei Zou,Wang Huisheng,Jun Chen,Xiao Dong,Jun Yang,Yuan Qiaojun +10 more
- 27 Mar 2020
TL;DR: A spring steel austenite grain display method is described in this paper, which consists of the following steps: (1) putting a sample at 850-900 DEG C, preserving heat for 10-40 minutes, quenching at a completely hardened cooling speed, tempering the cooled sample at 400-600 DEGC, and performing aircooling to room temperature; (2) cutting and embedding the cross section of the quenched sample in the rolling direction, and finally performing polishing.
Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model
Leilei Zou,Jiangshan Zhang,Qing Liu,Fanzheng Zeng,Jun Chen,Min Guan +5 more
- 05 Dec 2019
TL;DR: A data-driven regularized extreme learning machine (R-ELM) model is proposed for the prediction of carbon segregation index (CSI), which shows potential to evaluate online quality of steel billets and verifies the correctness and generalization ability of the R- ELM model.