Optimisation of electromagnetic stirring in continuously cast steel billets using ultrasonic C-scan imaging technique

2008 ◽  
Vol 35 (4) ◽  
pp. 288-296 ◽  
Author(s):  
M. Raj ◽  
J. C. Pandey
1981 ◽  
Vol 67 (8) ◽  
pp. 1297-1306 ◽  
Author(s):  
Yoshitaro UJIIE ◽  
Hirobumi MAEDE ◽  
Yukiyoshi ITOH ◽  
Shigeaki OGIBAYASHI ◽  
Hiroshi SEKI ◽  
...  

Author(s):  
Seenivasan Rajiah ◽  
Manjini Sambandam ◽  
Sethu Prasanth Shanmugam ◽  
Saju Vikraman ◽  
Rajendra Taticherla
Keyword(s):  

2021 ◽  
pp. 17-22
Author(s):  
A. Yu. Tretyak ◽  
◽  
Qiang Wang ◽  
Chun-Lei Wu ◽  
E. I. Shifrin ◽  
...  

Today, the most promising and effective method of quality control of the continuously cast billets is electromagnetic stirring of the melt. In this case, an important component is the effect of the stirring on the jet in the nozzle. Moreover, as research has shown, this method is highly dependent on the configuration of the inner channel of the nozzle. Research have shown that positive or negative taper of the inner surface of the nozzle allows to obtain different results after applying EMS. Taper control completely changes the pattern of the melt flow and its deceleration in the mold, especially when it is casting of large billets. The results of the research show that minor changes in the taper of the nozzle significantly increase the effect of EMS implementation, which is observed already at 0.27 % of positive the taper and increases to 0.54%.


2021 ◽  
Vol 15 (3) ◽  
pp. 381-386
Author(s):  
Miha Kovačič ◽  
Shpetim Salihu ◽  
Uroš Župerl

The paper presents a model for predicting the machinability of steels using the method of artificial neural networks. The model includes all indicators from the entire steel production process that best predict the machinability of continuously cast steel. Data for model development were obtained from two years of serial production of 26 steel grades from 255 batches and include seven parameters from secondary metallurgy, four parameters from the casting process, and the content of ten chemical elements. The machinability was determined based on ISO 3685, which defines the machinability of a batch as the cutting speed with a cutting tool life of 15 minutes. An artificial neural network is used to predict this cutting speed. Based on the modelling results, the steel production process was optimised. Over a 5-month period, an additional 39 batches of 20MnV6 steel were produced to verify the developed model.


1998 ◽  
Vol 69 (6) ◽  
pp. 228-236 ◽  
Author(s):  
Jacek Komenda ◽  
Gunilla Runnsjö

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